Method and computer-based sytem for non-probabilistic hypothesis generation and verification

ABSTRACT

The invention provides a method, apparatus and algorithm for data processing that allows for hypothesis generation and the quantitative evaluation of its validity. The core procedure of the method is the construction of a hypothesis-parameter, acting as an “ego” of the non-biological reasoning system. A hypothesis-parameter may be generated either based on totality of general knowledge facts as a global description of data, or by a specially designed “encapsulation” technique providing for generation of hypothesis-parameters in unsupervised automated mode, after which a hypothesis-parameter is examined for the concordance with a totality of parameters describing objects under analysis. The hypothesis examination (verification) is done by establishing a number of copies of a hypothesis-parameter that may adequately compensate for the rest of existing parameters so that the clustering could rely on a suggested hypothesis-parameter. The method of this invention is based on the principle of the information thyristor and represents its practical implementation.  
     This invention can be used as a universal computer-based recognition system in robotic vision, intelligent decision-making and machine-learning.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present invention is a continuation of and claims priorityfrom copending applications Ser. No. 09/655,519, filed Sep. 9, 2000 byLeonid Andreev, and entitled “Unsupervised automated hierarchical dataclustering based on simulation of a similarity matrix evolution”, whichis now U.S. Pat. No. 6,640,227, issued Oct. 28, 2003; and Ser. No.10/622,542, filed Jul. 24, 2003 by Leonid Andreev, and entitled“High-dimensional data clustering with the use of hybrid similaritymatrices”; the disclosures of which are herein incorporated in theirentirety by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] Not Applicable

REFERENCE TO A MICROFICHE APPENDIX

[0003] Not Applicable

[0004] Current U.S. Class: 706/6, 706/10, 706/12, 706/45, 706/61; 707/3,707/6, 707/100, 707/102, 707/104.1; 382/155, 382/159, 382/181, 382/190,382/225, 382/276; 702/190, 702/194

BACKGROUND OF THE INVENTION

[0005] 1. Field of the Invention

[0006] The present invention relates generally to the field of dataprocessing and, more particularly, to a method for hypothesis generationand verification (HyGV-method), allowing for intelligentdecision-making, image and sequence recognition and machine-learning.

[0007] 2. Description of the Background

[0008] 2.1. Hypothesis Testing in Mathematical Statistics

[0009] The subject of this invention pertains to such a vast area ofhuman cognitive activities that an exhaustive analysis of the backgroundof this invention would take an analytical description of the state ofthe art in a too diverse domain, including many humanitarian and exactsciences. The bulk of the works in this area deal with statisticalhypothesis testing, and, regretfully, there has been much less interestin the matter of testing of truly scientific hypothesis by applying theapproaches proposed by scientists who either attempted to tackle theproblem from the supra-science (i.e. philosophical) positions—forinstance, Popper's theory of hypothesis falsification (Popper, K. R.Logic of scientific discovery. London: Hutchinson, 1959)—or encounteredstatistical analysis problems of such complexity (for instance, inbiology, including and especially, ecology) that primitive mathematicalapproximation could be of no use in the understanding of principles ofinterrelations between variables that determine a real diversity ofobjects and phenomena.

[0010] The foundation of modern hypothesis testing, being the centralissue of modern mathematical statistics, was laid by Fisher (Fisher, R.A., 1925. Statistical Methods for Research Workers. Oliver and Boyd.London); later works by Neyman and Pearson (Neyman, J., and Pearson, E.S. 1933. On the problem of the most efficient tests of statisticalhypotheses. Philosophical Transactions of the Royal Society, A 131:289-337) instated certain modifications that gave it the generallyaccepted form. The probabilistic principles of setting forth and testinga hypothesis have been described in numerous works (cf. for example,Kendall, M., and Stuart, H., 1979. The advanced theory of statistics.Vol. 2. New York: Hafner; also: Royall, R. M. 1997. Statisticalevidence: a likelihood paradigm. Chapman and Hall. London, UK) andconstitute an essential part of the modem inference statistics. In anutshell (i.e. aside from the abundance of all the methods, approaches,techniques, and interpretations of mathematical statistics as presentedin numerous textbooks and used as the basis for numerous statisticalsoftware products), hypothesis testing is all about comparison between anull hypothesis and an alternative hypothesis. The former is to reflectthe absence of differences between population parameters, whereas thelatter is to state the opposite. An alternative hypothesis is acceptedif/when the null is rejected. The two hypotheses are compared based onnormal curves of probability distributions, and, therefore, none of themcan be conclusively proven or rejected, but one is eventually stated tobe more probable based on its higher probability degree.

[0011] It would be hard to put it better than D. H. Johnson did inHypothesis Testing: Statistics as Pseudoscience (presented at the FifthAnnual Conference of the Wildlife Society, Buffalo, N.Y., Sep. 26, 1998;published electronically on www.npwrc.usgs.gov), “I contend that thegeneral acceptance of statistical hypothesis testing is one of the mostunfortunate aspects of 20^(th) century applied science. Tests for theidentity of population distributions, for equality of treatment needs,for presence of interactions, for the nullity of a correlationcoefficient, and so on, have been responsible for much bad science, muchlazy science, much silly science. A good scientist can manage with, andwill not be misled by, parameter estimates and their associated standarderrors or confidence limits. A theory dealing with the statisticalbehavior of populations should be supported by rational argument as wellas data. In such cases, accurate statistical evaluation of the data ishindered by null hypothesis testing. The scientist must always give duethought to the statistical analysis, but must never let statisticalanalysis be a substitute for thinking! If instead of developingtheories, a researcher is involved in such practical issues as selectingthe best treatment(s), then the researcher is probably confronting acomplex decision problem involving inter alia economic considerations.Once again, analyses such as null hypothesis testing and multiplecomparison procedures are of no benefit.”

[0012] Statistical hypothesis testing has been heretofore viewed as theonly scientific approach to information processing. It determines boththe process of data processing and, to a greater extent, the approach tosetting up experiments and data selection (probability/nonprobabilitysampling). However, as mentioned by Anderson et al. (Anderson, D. R,Burnham, K. P., and Thompson, W. L. Null hypothesis testing: problems,prevalence, and an alternative. Journal of Wildlife Management 64(4):912-923), “over 300 references now exist in the scientific literaturethat warn of the limitations of statistical null hypothesis testing”.The number of such works had been exponentially increasing in the periodof the 40's to 90's of the past century.

[0013] This invention provides a method for hypothesis generation andverification (HyGV) that involves principles fundamentally differentfrom those employed in the statistical hypothesis testing methods; it isfree from the flaws of probabilistic approaches, can be applied inprocessing of any type of information, and it is exceptionally simple inuse. The method is based on the principle of the information thyristordesigned by us and described in Detailed Description of this invention.

[0014] 2.2. Hypothesis Generation and Verification

[0015] Hypothesis generation and verification is the basis of logicalthinking and of a well-grounded decision making. “Decision making” isone of the most frequently occurring terms in AI. Unless on eachoccasion of its use, an explanation is provided on what exactly isimplied by it in a given case, its general meaning is as fuzzy as itgets, up to a total lack of meaning. If what is meant by the term is anindependent, adequate and reproducible response to a change in a set ofalternative courses of actions, then any reliable measuring instrument(hyperbolically speaking, even a thermometer) should qualify as adecision-making method and apparatus. Or, if the impliedresponsibilities involve relieving the operator (a human-being) from thenecessity of screening and discarding false or unverified informationand to be able to advise a human-being on how to act in a particularsituation, then it is more of reference book. If a decision-makingsystem is supposed to be a “quick-learner”, then the question arises:what to learn and how? If the instruction/training is to be provided bythe human instructor, then such a device cannot be an independentthinker/decision-maker—it will remain a thermometer, howeversophisticated it may be, or a reference book, however regularly it isupdated. There is no learning part in such “training”. One and the samedecision applied to particular situations that are same in general,however, different in slight but important details may result inopposite outcomes, and, therefore, the user's failure to provide propercontrol over its “decision-making” artificial assistant may end poorlyfor the user.

[0016] There is no doubt that information systems significantlyinfluence users' decisions. However, the optimism of most researchers incomputer-based decision making goes far beyond that level—as formulated,for instance, by Larichev (Oleg I. Larichev. Close imitation of expertknowledge: the problem and methods Int. J. Inf. Technology and DecisionMaking, vol. 1, No. 1 (2002) 2742): “It is possible to say that one ofthe main goals of artificial intelligence consists in developingartificial systems that imitate expert reasoning”. It would also be safeto say that many intelligent human-beings would like to be able toimitate expert reasoning but they are not. Then where does all thisconfidence in artificial systems come from?

[0017] Acceptance of a decision is based on acceptance of a hypothesisthat provides an explanation regarding a certain phenomenon of an event,object, or person, as well as non-antagonistic alternatives thereof Ahypothesis is a verifiable statement which may include predictions. Aprediction is nothing else but a continuum of analogs of a givenphenomenon—even if the latter in reality may be a unique one. Value of aprediction depends on how correctly it can rank those analogs inaccordance with the probability of their occurrence depending oncircumstances.

[0018] Decision making involves several different stages, including thefollowing most important ones:

[0019] (1) recognition and understanding of a problem on which adecision has to be made, or formulation of an objective of adecision-making task;

[0020] (2) hypothesis generation, i.e. construction of a series ofvariants of potentially applicable decisions supposedly including anoptimal one;

[0021] (3) search for information that may be used for hypothesisverification;

[0022] (4) hypothesis verification.

[0023] As this invention provides a method and system for unsupervisedhypothesis generation and verification, in this context it is importantto elaborate on the matter of which of the stages of the computerizeddecision-making process can in principle be implemented as anunsupervised operation. Such an analysis of the decision-making stageswill facilitate the generation of a hypothesis on the issue of whycomputers are still unable to make decisions on their own, and whetherthere may be any solutions for this problem.

[0024] We will start with the last of the aforesaid basic stages ofdecision making—i.e. hypothesis verification. There exist many differentviewpoints regarding this part of the decision-making process; forinstance, Popper's opinion that it is all about creative intuition whichcannot be governed by logic (Popper, Karl. The logic of scientificdiscovery. New York: Basic Books, Inc. 1959), or, quite a polar view ona hypothesis as an expression of the relationship between two (or more)variables (McGuire, W. J. 1989. In: The Psychology of Science:Contributions to Metascience. Ed. B. Gholson, A. Houts, R. Neimeyer, W.R. Shadish, pp 214-245. New York: Cambridge Univ. Press). If hypothesisverification can be brought to comparison of values of differentvariables, then this task is well within computer's competence. Same istrue for the third stage—information search—which, by definition, is thearea where computers outperform humans in speed and efficiency. Thesecond stage—hypothesis generation—is closely connected with the firststep in decision making, i.e. the understanding and formulation of anobjective, and therefore is extremely difficult for computerizedimplementation. Nevertheless, there are many factors to support thefeasibility of that task. See, for example, McGuire's discussion ofcreative hypothesis generation on strategic and tactical levels and thedescription of 49 heuristics, including 5 types and 13 subtypes, thatare used by psychologists and can be taught (McGuire, W. J. (1997)Creative hypothesis generating in psychology: some useful heuristics.Annual Review of Psychology, v. 48, pp. 1-30). In the followingdisclosure of this invention, we will show that not only iscomputer-based hypothesizing possible, but it is also possible todevelop a computer-implemented imitation of approaches used in human wayof thinking. As far as the first stage of the decision-making process isconcerned, it involves that very unique function that can be performedonly by humans and (at least as of today) not by computers. Apparently,this is the key aspect that has to be explored before taking thechallenge of the “thinking computer” idea. One of the many issuesinvolved in this problem is pivotal in the context of this invention,which, in its turn, has been conceived as a logical result of thedevelopments presented in the related patent and copending application.

[0025] As is well-known, different individuals can (and, more often thannot, do) make different decisions regarding one and the same situation.When two experts express two different or opposite to each other'sopinions on a same matter, a person seeking an expert opinion andfamiliar with the individual styles of each of the experts' performancewill only gain from the obtained results. For instance, one of theexperts may be too conservative and cautious in judgments, whereasanother may be overly categorical. A common feature of both of them isindividuality, i.e. each of them has a unique and specific way of apsycho-physiological response, philosophical view on phenomena understudy, preferences in logical approaches, etc.—all of which can be takeninto account and used in making a final decision. A bad expert's opinionmay appear to be no less useful if such expert's style is reproducible.Contrarily, a computer does not have the individuality, and its“brain”—the software—is a composite product of the humankind and isdeveloped by large groups of programmers.

[0026] Individuality or “ego” can be interpreted in different ways. Forinstance, computers manufactured by a perfectly same technology maystill have slight differences, each of its own, and, therefore, can beviewed as “individualities”—if non-individuality is understood only assharing exactly same set of properties of objects. However, there isalso another understanding of individuality, as, for instance, appliedto a human-being taking a road of his own and capable of independentthinking and judgment; and in the context of individuality it does notmatter whether or not the thinking, judgments and decisions are correct.We imply this interpretation of individuality when stating that acomputer does not have it.

[0027] 2.3. What It Takes to “Raise” the AI

[0028] Many AI terms that have been around for decades by now still lackclear and explicit definitions of what exactly is implied by a giventerm—which is not surprising as the whole domain of AI is aboutimitation of something which itself has not yet been fully explored bythe science (cf. for instance, John Searle (1992) The Rediscovery of theMind. MIT Press, Cambridge, Mass.). Thus, from its very onset, the AIresearch has been oriented toward the effect rather than the cause,toward the imitation of the brain's unique abilities without theunderstanding of their nature. And, of course, the AI is expected towork independently, i.e. relieving the human operator from the necessityto control the AI's every step. This cocktail, made of materialism andCartesian ideas, has been served to several generations of AI student,although everybody in the field understands by now that the modern useof the term “artificial intelligence” is more marketing than scientific.In general, all what computer science has so far come up with on theissue of imitating the human or animal brain processes is a vocabulary.Take, for instance, artificial neural network (ANN) systems after theMcCulloch-Pitts model of the neuron based on an intuitive view of howcharge accumulation occurs on a cell membrane and how it influencessynapse strengths. Not only readers of popular scientific literature,but also many researchers in artificial neural network are convincedthat ANN is indeed the imitation of the work of brain neurons. Leavingalone the fact that the whole concept is purely a product of computerprogramming and mathematics and that the word “neuron” in this contextis just a symbol of a future goal and by no means an assertion of anyreal achievement, there is yet another problem: even if computerengineering can describe and simulate the synapse formation andtransmission, how can it describe and simulate what is still unknown toneuroscience: how is specific information communicated from one neuronto another?

[0029] With all its obvious interest in biological terminology, computerscience omits to focus on really important features of autonomousself-referent biological systems as the mammalian brain, while it iswell-known that many of those features play the key roles in thefunctioning of living systems. There is an undeniable truth about thehuman brain activity, and failure to realize or remember that truthinevitably results in failure in fulfilling the task of the realisticsimulation of the human brain activity. That truth is so simple andtrivial on the surface that it does not catch the attention of thecomputer science community whose hope for creation of artificialintelligence—be it through computation speed breakthrough, computermemory expansion, or advances in programming art (for instance, productsof the artificial neural network concept)—never dies. However, it isobvious that there is nothing yet in the computer science field thatcould give hope for development of a computer system that would be ableto make independent decisions on what is right and what is wrong. Eventhe strong believers in the future of artificial intelligence realizethat the computing power in the fifth or sixth generation cannot, byitself, guarantee a breakthrough in the AI field.

[0030] The simple and trivial truth, referred to above, consists in thefact that any living system—including, of course, the brain as the mostcomplex domain in the system of the living substance—has a highlycooperative infrastructure. Cognition is a biological phenomenon, and itcan be understood only as such. Consciousness cannot be explained bymerely making a list of all its properties. Metabolic systems of livingorganisms involve lots of biochemical processes whose performances areultimately coordinated. Even a small failure in a minor “department” ofa metabolic system (“minor” from a biochemist's anthropomorphicviewpoint) may become a debilitating or lethal factor for a system. Nocomputer program attempting to imitate the processes occurring in livingorganisms and, especially, in the brains, the most complex part of them,can provide for that level of coordination, and it is clear why. Thehuman brain has mysterious properties, and no less mysterious are thoseof the human body infrastructures that support the brain functioning—forinstance, the haematoencephalic barrier whose role is not to allowcertain substances that can damage the brain work to penetrate the nervecells. A computer program that can at any point sustain artificiallymade commands without a complete loss of its functionality will never beable to imitate the brain properties. Should it ever happen that acomputer program with the functionality similar to that of human brainis created, it will consist of a set of algorithms that provides acontinuous metabolic cycle with the highest level of cooperation andcoordination between its constituent parts.

[0031] Complex computer programs are developed in a programming stylethat to a large extent corresponds to what could be defined by aneclectic notion of “compromise logic”. As software developers' keypriority is the achievement of a technical objective rather thanmaintaining a certain wholesome logic, it often happens that startingwith the very early stages of a computer program development, a unifiedalgorithmic core can no longer be maintained and breaks into a multitudeof artificially joined individual algorithms. Execution results providedby individual algorithms are further either used, or ignored, orrejected, depending on how well they work towards the solution oftactical and strategic tasks in the context of a given computer program.Thus developed a computer program can be compared to music withoutmelody; its individual components often become mutually antagonistic,and to eliminate the antagonism, developers resort to “prematuremathematization” (Russel, S. [1997] Rationality and intelligence.Artificial Intelligence Journal, 94 (1-2) 57-77). The latter, whileresolving particular local problems, inevitably creates new problems,and swiftly fills up the whole space of a program where logicalcontinuity of its components is missing. Thus, the attempts to cope withthe growing complexity of computer programs lead to creation of morecomplex programs.

[0032] Full cooperation between all of the algorithms of a computerprogram is an extremely difficult task, and without its implementation,no program that can qualify for the role of the brain's artificialcounterpart. A truly cooperative system of algorithms does not toleratecommands that are alien to its environment, however important theirexecution may be in the context of a program's performance or in theview of its designer. Simply put, an algorithm that effectively imitatesthe brain can be emulated by no other algorithm but itself In general,this constitutes that truth which is so trivial that it remains simplyignored.

[0033] Another simple but important truth, relevant in the issue of theefficiency of computer-implemented learning, consists in the fact thatcognition is a product of interaction between deduction and induction.Over two thousand years of experience and knowledge generated by themankind's best think-tanks testify to the fact that these two oppositelydirected processes underlie the actual process of cognition. Howeverintensely has this issue been investigated throughout the pastcenturies, we have yet to understand how these two fundamentalmechanisms interact in the brain. But the fact of the matter is thatthere is spontaneous interaction between deduction and induction, andthey are inseparable.

[0034] 2.4. Algorithmic Foundation of This Invention

[0035] Our research and development in AI, or—using a more correct butless common term—non-biological intelligence, NBI (see more informationon the related work on http://www.matrixreasoning.com), has been basedon the understanding of the fact that without the implementation of twoaforementioned features of the brain—functionality cooperation andorganic spontaneity of the relationship between deductive and inductiveprocesses (or—speaking in computer science language—without analgorithmically holistic approach)—no imitation of the brain activity ispossible. This ideology led us to development of a system ofinterrelated algorithms for identification, differentiation andclassification objects described in a high-dimensional space ofattributes, which further has been used as the underlying methodology inthis invention. The said methodology, comprising the evolutionarytransformation of similarity matrices (U.S. Pat. No. 6,640,227, October2003, by L. Andreev) as a new universal and holistic clustering approachthat provides a solution to most complex clustering problems, is basedon quite a simple algorithm that can be defined by a commonly knownprinciple of“the golden mean”.

[0036] The method for evolutionary transformation of similarity matricesconsists in the processing, in one and the same fashion, of each cell ofa similarity matrix so that a similarity coefficient between each pairof objects in a data set is replaced by a ratio of a similaritycoefficient between each of objects in a pair and a mean value ofsimilarities between each of two objects whose replacement similaritycoefficient is under computation and all other objects of a matrix. Thealgorithm of the process of evolutionary transformation of a similaritymatrix is based on the following formula: $\begin{matrix}{{S_{A,B}^{T} = \left( {\prod\limits_{i = 1}^{n}\quad \frac{\min \left( {S_{i{(A)}}^{T - 1},S_{i{(B)}}^{T - 1}} \right)}{\max \left( {S_{i{(A)}}^{T - 1},S_{i{(B)}}^{T - 1}} \right)}} \right)^{\frac{1}{n}}},} & (1)\end{matrix}$

[0037] where S^(T) _(A,B). is a binary similarity coefficient aftertransformation No. T; “n” is a number of objects associated with amatrix; A, B, and i are objects associated with a matrix; “min” and“max” mean that a ratio of S^(T) _(i(A)) to S^(T) _(i(B)) are normalizedto 1. The algorithm for such transformation is repetitively applied to asimilarity matrix till each of similarities between objects within eachof the clusters reaches 100% and no longer changes. In the end, theprocess of successive transformations results in convergent evolution ofa similarity matrix. First, the least different objects are grouped intosub-clusters; then, major sub-clusters are merged as necessary, and,finally, all objects appear to be distributed among the two mainsub-clusters, which automatically ends the process. Similarities betweenobjects within each of the main sub-clusters equal 100%, andsimilarities between objects of different sub-clusters equal a constantvalue which is less than 100%. The entire process of transformation mayoccur in such a way that while similarities within one sub-cluster reachthe value of 100% and stop transforming, another sub-cluster stillcontinues undergoing the convergent changes and take a considerablenumber of transformations (in which the objects of another sub-clusterare no longer involved). Only after the convergent transformation of thesecond sub-cluster is complete, i.e. when similarities between itsobjects reach 100%, and similarities between objects of the twosub-clusters clusters is less than 100%, an entire process ofevolutionary transformation of a similarity matrix is over. In thedescribed process, there is no alternative to the sub-division of allobjects of a data set into two distinctive sub-clusters. Any object thatmay represent a “noise point” for any of the major groups of objects ina data set of any degree of dimensionality gets allocated to one ofsub-clusters.

[0038] Conversely, the above described convergent evolution may also berepresented as divergent evolution and reflected in the form of ahierarchical tree. However, the mechanism of the algorithm forevolutionary transformation involves the most organic combination of theconvergent and divergent evolution (or deduction and induction based oninput information about objects under analysis). For that purpose, eachof the sub-clusters formed upon completion of the first cycle oftransformation is individually subjected to transformation, whichresults in their division into two further sub-clusters, respectively,as above described; then, each of the newly formed four sub-clustersundergoes a new transformation, and so on. This process, referred to as‘transformation-division-transformation’ (or TDT) provides for the mostrational combination of the convergent (transformation) and divergent(division) forms of the evolution process, in the result of which anentire database undergoes multiple processing through a number ofprocesses going in opposite directions. The said combination ofprocesses is not regulated and is fully automated, autonomous andunsupervised; it depends on and is determined by only the properties ofa target similarity matrix under analysis, i.e. by input data and anapplied technique of computation of similarity-dissimilarity matrices.In other words, the ETMS algorithm is based on “uncompromising” logicthat cannot be manipulated by arbitrarily introduced commands, whichresults in the fact that the efficiency of the ETMS-method greatlydepends on how adequate and scientifically well-grounded are thetechniques used in presentation of input data (i.e. computation ofsimilarity matrices). Thus, for the evolutionary transformation methodto be independent from the operator's will and truly unsupervised, thesimilarity matrix computation must be based on a procedure that does notdepend on the type of input data.

[0039] Some of the approaches applied in many of the widely usedapplications for the purpose of establishing similarity-dissimilarity ofobjects described in high-dimensional space of attributes clearlyrepresent a forced solution used for the lack of proper techniques andare simply nonsensical. For instance, there is a widely known notion ofthe “curse of dimensionality” which refers to a dramatic dependency ofparameterization of distances between attributes on their dimensionality(Bellman, R. 1961. Adaptive Control Process: A Guided Tour. PrincetonUniversity Press.). Understandably, this dependency catastrophicallyincreases in a super-space, resulting in a situation when the most thatcan be done about similarities-dissimilarities is the standardization ofconditions for comparison of similarities on a presumption that “objectsin a set have otherwise equal status”, which by definition cannot beconsidered as an acceptable methodological platform. For instance, it iscustomary to use Euclidean distances to determine similarities (betweenobjects) as vectors in n-dimensional spaces of parameters even if theyare described by different dimensions—despite the elementary truth thatthis is grossly unscientific. This inadmissible compromise furthercreates multitude of problems, starting with the “curse ofdimensionality” and up to the necessity of entering special constraintsfor a computer program to avoid the use of Euclidean distances where itis absurd.

[0040] In the meantime, there is quite a simple solution thateffectively and completely takes care of the problem of unsupervisedautomated computation of similarity matrices for objects described byany number of parameters. The solution, described by us in a copendingpatent application entitled “High-dimensional data clustering with theuse of hybrid similarity matrices”, consists in the following. So-calledmonomer similarity matrices according to each of parameters describing agiven set of objects are computed for a set of objects, after which themonomer matrices (whose total number corresponds to a total number ofparameters) are hybridized. If we have a set of monomer similaritymatrices (M) where each of the matrices is calculated based on one ofthe parameters, i.e.

M(a), aε{1,2, . . . , n}  (2),

[0041] then, hybridization of the matrices is performed by the formula:$\begin{matrix}{{{Hij} = \left( {\prod\limits_{a = 1}^{n}\quad {M(a)}_{ij}} \right)^{1/n}},} & (3)\end{matrix}$

[0042] where Hij is a value of hybrid similarity between objects i andj. Thus, the computations in both the ETSM algorithm and theabove-referred procedure for preparing hybrid similarity matrices forthe ETSM-method are based on a simple operation of calculation of meanvalues. Hybridization of matrices leads to the natural fusion of objectpatterns in terms of their variables' values. Clearly, hybridization canbe done on similarity matrices that have been computed based on any typeof attributes (categorical, binary, or numerical). Since attributesconverted into units of a monomer similarity matrix no longer have anydimensionality, the above referred procedure for hybridization ofmonomer similarity matrices can be used as a methodological basis forcomparison of attributes of any kind and nature.

[0043] As a result of development of monomer similarity matriceshybridization technique, it has become possible to add to a hybridmatrix any numbers of copies of individual parameters, thus to find outweights of individual parameters in a totality of all parameters thatdescribe a given set of objects. The parameter multiplication methoddescribed in a copending application by L. Andreev (“High-dimensionaldata clustering with the use of hybrid similarity matrices”) hasprovided the grounds for the method of this invention.

[0044] The final issue that ought to be discussed in the context of thebackground of this invention is the technique for monomer similaritymatrix computation. As monomer matrix computation is based on a singleparameter, it causes, for instance, a Euclidean distance automaticallytransform into the city-block metric. The copending application by L.Andreev “High-dimensional data clustering with the use of hybridsimilarity matrices” provides two types of metrics to be used incomputation of monomer similarity matrices—the R- and XR- metrics. TheR-metric (“R” for “ratio”) is calculated by the formula:

R _(ij)=min(V _(i) , V _(j))/max(V _(i) , V _(j))   (4),

[0045] where V_(i) and V_(j) are values of parameter V for objects i andj. Here, similarity values are calculated as the ratio of the lowervalue to the higher value of a parameter of each of the two objects.Thus, values of the R similarity coefficient vary from 0 to 1.

[0046] The XR-metric (“XR” stands for “exponential ratio”) is calculatedby the formula:

XR _(ij) =B ^(−|Vi−Vj|)  (5),

[0047] where V_(i) and V_(j) are values of parameter V for objects i andj, and B (which stands for “base”) is a constant higher than 1. Valuesof the XR similarity coefficient also vary from 0 to 1.

[0048] R-metric is optimal for description truly or quasi-equilibriumsystems where attributes reflect a signal strength, concentration,power, or other intensiveness characteristics. XP-metric is optimal fordescription of non-equilibrium systems where attributes reflect a systemshape for operations in spatial databases, a distance between individualpoints within a system, or other extensiveness characteristics.

SUMMARY OF THE INVENTION

[0049] The present invention provides a novel method for hypothesisgeneration and verification (HyGV-method) to be applied in dataprocessing as a universal solution for problems of computerizeddecision-making, machine learning, as well as a wide range of image andpattern recognition problems—starting with a search for any type ofsequences, up to robotic vision problems. One of the most importantadvantages of the method of this invention lies in the fact that, alongwith the exact recognition of a query object, it provides analog rankingin a manner that considerably simplifies the approach to such complexproblems as intelligent data understanding and machine learning. Theproposed method allows for the optimization of a decision making processand for automated evaluation of decision validity. This invention canalso be used in search engines and related tasks, e.g. retrieval ofdocuments, etc.

[0050] The main concept that underlies the present invention consists inthe generating of a certain estimation scale for objects underinvestigation—in the form of a hypothesis that, once generated, servesas an additional parameter and, therefore, will be hereunder referred toas “hypothesis-parameter” (HyPa) which represents an abstractreflection, in a digital form, of regularities existing in a set ofobjects described in a high-dimensional space of parameters. HyPa mayeither result from a preliminary analysis of a given set (e.g. aclustering result), general knowledge, a “wild guess”, etc., or it maybe automatically generated for a single object in a set of objects. Inthe latter case, it consists in construction of a so-called “capsule ofclones” of a reference object (here and further in this specification, a“reference object”, or a “query object” is an object to be located andidentified; and a “target object” is an object under scrutiny at a givenmoment of a database analysis, i.e. an object that is checked andinvestigated in order to establish whether or not it may appear to bethe “reference object” or one of its closest analogs)—i.e. anartificially generated hypothetical set of similar objects—clones of areference object—whose parameter values differ, according to a certainprinciple, from those of a reference object. A “capsule” may include anynumber of analogs of a reference object; however, an optimal capsuleusually including up to 5-7 analogs. A “capsule” is created forconstruction of HyPa for a singular reference object, with the furtheruse of a created HyPa as a digital parameter, along with otherparameters describing a given object.

[0051] The central procedure of the method of this invention is theestablishing of a number of copies of a “hypothesis-parameter” to beadded, during clustering by method for similarity matrix evolutionarytransformation (U.S. Pat. No. 6,640,227, October 2003, by L. Andreev),to an initial pool of parameters to neutralize the effect of a totalityof initial parameters so as a clustering result is the same as it wouldbe if based on just one parameter, i.e. HyPa. The higher is the numberof required HyPa multiple copies (multiplication number M), the less isits resemblance to a reference object. Natural logarithm of an HyPamultiplication number (M) is referred to as implausibility number. A lnM value equals 0 when an analyzed object and an object whose HyPa isused for comparison are identical in terms of given parameters. The InM, being a sort of dissimilarity criterion, allows for outlining a spaceof close analogs and thus makes machine-learning process easier.Implausibility number is an exceptionally sensitive criterion in searchfor analogs of a query object, and it nonlinearly increases in case ofobjects whose parameter values differ from those of a reference object.

[0052] The HyGV-method presented by this invention is based on theprinciple of the information thyristor, hereunder referred to as“infothyristor”, and described below in the Detailed Description sectionof this specification.

[0053] The examples provided in this disclosure demonstrate a peculiarmanner in which a reference object's analogs are selected according tothe method of this invention—very much similar to the human brain'snatural manner to select from a diversity of objects and phenomena andkeep in memory those that deserve remembering. However, in machinelearning, such registering of information is necessary but insufficient.There has be a certain mechanism to ensure a proper process of analogselection based on required degree of similarities between objects to beselected as analogs and a standard chosen by us as a focus of learningand provided with an expert opinion.

[0054] The realization of the method of the present invention has becomepossible as a result of development of two earlier inventions by L.Andreev: “Unsupervised automated hierarchical data clustering based onsimulation of a similarity matrix evolution” (U.S. Pat. No. 6,640,227,October 2003), and a copending application titled “High-dimensional dataclustering with the use of hybrid similarity matrices”. The objectiveautonomous and unsupervised automated generation of HyPa for individualobjects is based on the method for evolutionary transformation ofsimilarity matrices (ETSM). HyPa multiplication numbers required forcompensation of an entire pool of initial parameters can only beestablished with the use of the procedure for hybrid matrix computationand involve the use of the metrics proposed in the same invention.According to the method for matrix hybridization, a similarity matrixfor a set of objects described in a high-dimensional space of parametersis computed as a product of hybridization of monomer similarity matriceseach of which is computed based on one parameter and isdimensionless—which is why any number of copies of object's individualparameter may be introduced into a hybrid matrix.

[0055] This invention can be effectively applied to data processing andintelligent data understanding tasks in virtually any research andpractical area. The detailed description of this invention providesexamples of the application of the HyGV-method in demographic studies,climate research, biometrics, and image recognition. The method of thisinvention allows for analysis of objects described by any number ofparameters: for instance, the example of demographic analysis providedbelow deals with the data on 220 countries, each described by 51parameters; the climatic data analysis illustrated below deals with thedata on 245 U.S. cities and locations in 50 states, described by 108parameters. Processing time per object is practically constant.

[0056] In this disclosure, it is demonstrated that the ETSM-method(according to copending application), used as an engine for theHyGV-method of this invention, and the HyGV-method itself correlate witheach other as intuition and reasoning, thus providing a platform fordevelopment of a working model of non-biological intelligence and themechanisms of deductive and inductive machine self-learning.

[0057] All of the techniques contained in this invention are easy inrealization, and their computer-based implementation is done on aregular PC system. It is also important that the algorithms underlyingthe method of this invention are based on iterative uniformcomputational operations and, therefore, are multiprocessor-friendly,thus making this method efficient in real-time complex data processing.

BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The foregoing and other aspects and advantages of the presentinvention will be better understood from the following detaileddescription of the invention with reference to the drawings in which:

[0059]FIG. 1 is a flow diagram showing the algorithmic architecture ofMeaningFinder™ computer program as the implementation of this invention.Steps 103 through 108 are covered by U.S. Pat. No. 6,640,227“Unsupervised automated hierarchical data clustering based on simulationof a similarity matrix evolution” by Leonid Andreev, and a copendingapplication “High-dimensional data clustering with the use of hybridsimilarity matrices” by Leonid Andreev.

[0060] FIGS. 2A-2C show the examples of hypothesis-parameter (HyPa)clustering trees. In the notations, the first numbers stand for numbersof subclusters formed out of the HyPa: thus, tree A (FIG. 2A) has 3subclusters; tree B (FIG. 2B), 4 subclusters; and tree C (FIG. 2C), 6subclusters. The numbers in brackets correspond to numbers of nodes inthe clustering trees. In FIG. 2C, the subcluster notations include(numbers in brackets) examples of values assigned to respectivesubclusters in the HyPa.

[0061]FIG. 3 is a flow diagram showing the principles of functioning ofthe method of this invention. Explanations are provided in the detaileddescription.

[0062]FIG. 4 is an illustration of various kinds of trees resulting fromclustering of: a hypothesis-parameter in the form of a capsule of clonesof a reference object (block 401); the hypothesis-parameter after theaddition of reference object “R” (block 402). Trees A and C wereobtained by clustering of the hypothesis-parameter as the onlyparameter; and trees B and D were produced by clustering involving allparameters and excluding the hypothesis-parameter (cluster notations areshown in square brackets).

[0063]FIG. 5 is a schematic diagram showing the operation of theHyGV-method as information thyristor. The notations in square bracketsindicate the subclusters obtained by clustering based on all parametersexcept for the hypothesis-parameter; while those in parentheses indicatethe subclusters based on the use of only the hypothesis-parameter. Thesubclusters shown in double brackets (square and round) are the resultof the use of all parameters and the HyPa. “T” is a target object;“A_(R)” and “B_(R)” are components of the capsule of clones created forthe reference object “R”; and “M” is the multiplication number.

[0064]FIG. 6 shows 3D-diagrams of subclusters produced by clustering of33 scattered points. A is the initial diagram. B is the diagram wherethe X-ordinate value of the asterisked point has been decreased by 500%.Enumeration of subclusters shown in the diagrams was used inconstruction of the HyPa (see FIG. 2C).

[0065]FIG. 7 is a plot showing changes in plausibility number −ln Mwhere M is multiplication number which depends on percent of deviationfrom the X-coordinate value for the asterisked point in FIG. 6.

[0066]FIG. 8 shows the relationship between ln M_(ab) values computedfor 245 cities of 50 states of the U.S.A., by using San Diego, Calif.,as a reference object.

[0067]FIG. 9 shows the relationship between ln M_(ab) values computedfor 245 cities of 50 states of the U.S.A., by using San Diego, Calif.,and Charleston, S.C., as reference objects.

[0068]FIG. 10 is a 3D-diagram showing the grouping of 80 countries,using 51 demographic parameters and based on implausibility numbers (lnM). The hypothesis-parameter was constructed based on the results ofclustering performed by the ETSM-method. Indices “a”, “ba”, “bbaa”,“bbab”, and “bbb” correspond to the following subclusters: “a”, Egypt,Kuwait, Morocco, and Saudi Arabia; “ba”, Israel; “bbaa”, Bulgaria andLatvia; “bbab”, Croatia and Czech Republic; and “bbb”, The Netherlands,Norway, Sweden, and UK. Subclusters' digitalization in thehypothesis-parameter was as follows: a=5, ba=4, bbaa=3, bbab=2, andbbb=1. Group A consists of 41 countries with predominantly Muslimpopulations (dark dots); while group B includes 17 capitalist countrieswith predominantly Christian populations (dark dots), as well as 21former Soviet bloc countries with predominantly Christian populations(open dots).

[0069]FIG. 11 is a 3D-diagram showing the grouping of 80 countries,using 51 demographic parameters and based on implausibility numbers (lnM). The hypothesis-parameter was constructed based on the concept ofpredominant religions in the countries under analysis. Group A includes35 countries with predominantly Muslim populations, excluding those thatmake group B; group B includes: (1) Azerbaijan, (2) Turkey, (3) Tunisia,(4) Kazakhstan, (5) Albania, and (6) Lebanon. Group C joins together 17European capitalist countries with predominantly Christian populations.Group D includes 17 former Soviet bloc countries with predominantlyChristian populations, excluding those that make group E. Group Econsists of Czech Republic, Bulgaria, Slovenia, and Hungary.

[0070]FIG. 12 is a 3D-diagram showing the grouping of 74 countries basedon 51 demographic parameters. Analysis conditions are the same as in theexample illustrated by FIG. 11, except that the list of countries underanalysis excluded groups B and E and included 5 other countries withpredominantly Chinese populations.

[0071]FIG. 13 shows the results of clustering by evolutionarytransformation of a similarity matrix computed based on four differentvalues of multiplication numbers corresponding to particular countriesin the hypothesis-parameter: a=12 (Saudi Arabia), ba=5 (Israel), bb=2(Russia), and bb=3 (Italy). Hypothesis generation was performed by theHyGV-ID method described in this specification (see also FIGS. 10-11),the similarity matrix was computed using the R-metric (equation 4 ofthis specification).

[0072]FIG. 14 shows the relationship between multiplication numbers M(2)and M(3) computed for 17 countries described by 34 parameters based onrespective population pyramids. Hypothesis generation was performed bythe HyGV-CC method. Two different capsules of clones were created, thereference object being Saudi Arabia; each capsule consisted of 10clones, and each clone was different from the preceding one by 0.4% and1.6%, respectively (shown as open and dark dots, respectively, in theplot). HyPa was digitalized as follows: a (clones 9, 10)=10, ba (clone5)=2, and bb (clones 1, 2)=1. HyPa values for each of the objects underanalysis were: 2, in which case, the multiplication number was denotedas M(2), and 3, respectively, with the multiplication number of M(3).

[0073] FIGS. 15A-15F illustrate searches for closest analogs of 6countries being query (reference) objects (shown at the coordinates'points of origin; implausibility numbers=0). The implausibility numberswere computed by the HyGV-CC method, for 41 countries with predominantlyMuslim populations described by 34 parameters of population pyramids.

[0074]FIG. 16 is a plot-map of 220 countries, based on 34 parameters ofpopulation pyramids using Russia and Saudi Arabia as reference objects.Implausibility numbers were computed by the HyGV-CC method. Dark dotscorrespond to Russia and 13 countries of the former Soviet bloc showingthe closest proximity to it.

[0075]FIG. 17 is a schematic image of a human body, referred to, furtherin this specification, in the examples of the application of theHyGV-method in image and gait recognition. The parameters, describingthe image, were computed as vertical distances from the skull top point(1) to: mandible (2), right and left clavicle (3R and 3L), sternumcenter (4), sternum bottom (5), right and left elbow (6R and 6L), lumbarvertebra (7), right and left wrist (8R and 8L), right and left handfinger tips (9R and 9L), sacrum (10), right and left hip (11R and 11L),right and left knee (12R and 12L), right and left toe (13R and 13L).

[0076]FIG. 18 shows 42 out of 75 artificially generated images of humanbody poses used in the example of the application of the HyGV-method inimage recognition. The rest of the images are shown in FIGS. 19-24 and43.

[0077]FIG. 19 shows the result of search for closest analogs of imagesof human body poses in a database of 75 images, by the HyGV-CC method.Capsules of clones were constructed as C3(2) (see FIG. 2A) anddigitalized as follows: a (1)=1, aa (2)=5, ab (2)=10 (numbers of clonesin capsules are indicated in brackets). The XR-metric (B=1.50) (seeequation 5 in the specification) was applied. The reference object(query) was an image of a human figure with its upper body bent forwardalmost at the right angle, hands stretched forward, and both legsstraight.

[0078]FIG. 20 shows the result of search (among 75 images) for closestanalogs of a human body pose with hands up and legs straight. All otherconditions are the same as in the search illustrated by FIG. 19.

[0079]FIG. 21 shows the result of search (among 75 images) for closestanalogs of a human body pose with the right leg and right hand raised.All other conditions are the same as in the search illustrated by FIG.19.

[0080]FIG. 22 shows the result of search (among 75 images) for closestanalogs of a human body pose with hands down and legs straight. Allother conditions are the same as in the search illustrated by FIG. 19.

[0081]FIG. 23 shows the result of search (among 75 images) for closestanalogs of a human body in a sitting position. All other conditions arethe same as in the search illustrated by FIG. 19.

[0082]FIG. 24 shows the result of search (among 75 images) for closestanalogs of a human body lying on the stomach. All other conditions arethe same as in the search illustrated by FIG. 19.

[0083]FIG. 25 shows the relationship between similarity coefficientsS_(a) and S_(ab) computed by equation 6 for the human body pose shown asa query object in FIG. 24. The internal standard was the pose shown inFIG. 22 as a query.

[0084]FIG. 26 is a table demonstrating the additivity of multiplicationnumber values computed by the HyGV-method for human body poses No. 1-14shown in FIG. 18. The capsule of clones was made for the referenceobject shown as a query in FIG. 23. Out of 18 points used as parameters(see FIG. 17) of the artificially generated human body images, 7referred to the left half of the body; 7, to the right half; and 4, tothe torso center.

[0085]FIG. 27 shows 45 artificially generated schematic images(“frames”) of a human figure captured at various moments of the processof walking, used for demonstration of application of the HyGV-method ingait recognition.

[0086]FIG. 28 is a plot showing how the multiplication number (M) valueschange in accordance with the walking motion frames shown in FIG. 17.The reference object was the first frame in FIG. 27. 15 parameters(vertical distances from the skull top, as a zero point, to each of thepoints shown in FIG. 27, except for points 4, 5, and 7) were used in theanalysis. Curve A reflects the M values established based on all 15parameters; curve B shows the M values computed for respective totals ofeach right and left measurements—7 parameters corresponding to each halfof the body, and point 4 (see FIG. 17); and curves C and D reflect the Mvalues corresponding, respectively, to the left and right halves of thebody.

[0087]FIG. 29 is a plot showing how the multiplication number (M) valueschange in accordance with the walking motion frames shown in FIG. 17.The reference object was frame 1 in FIG. 27. Curve A is based on 9parameters corresponding to the distances from the skull top to points11 through 13 (see FIG. 17). Curve B reflects the walk dynamics computedwith the use of 4 extra copies of the left knee parameter. Curve C wasobtained after the addition of 4 extra copies of the right kneeparameter.

[0088]FIG. 30 illustrates an example of a table used for computation ofa hybrid similarity matrix and identification of a certain string in asequence of n-number of elements (e). Here, “f” is a length of a targetstring; “k” is the No. of an element of the sequence that is the firstelement of a target string; and CC(k) is a capsule of clones for thestring of “k+f−1” elements. The data in the table are changing as the“frame” is moving along the sequence under analysis.

[0089]FIG. 31 is an illustration of an artificially generated signalpattern. Sections A (40-59) and B (80-99) are target objects forsequence recognition.

[0090]FIG. 32 illustrates the result of sequence A (see FIG. 31)recognition by the method of this invention. The ln M value drop to 0indicates that the target sequence has been located.

[0091]FIG. 33 illustrates the result of sequence B (see FIG. 31)recognition by the method of this invention. The ln M value drop to 0indicates that a target sequence has been located.

[0092]FIG. 34 shows the relationship between the identificationuncertainty computed for sequence B (see FIG. 31) by equation (7) ofthis specification and the deviation from the value of the signal attime-point 83 (the asterisked point in FIG. 31).

[0093]FIG. 35 shows an artificially generated binary sequence. Thesection in bold is a reference string.

[0094]FIG. 36 is a table for computation of a similarity matrix used inbinary sequence recognition.

[0095]FIG. 37 is a plot showing the changes in multiplication numbers asthe screening frame in the form of the binary string (see FIG. 36) ismoving along the binary sequence shown in FIG. 35. Dark dots correspondto multiplication numbers for the 15-bit binary string screening frame(k=35, f=15); open dots, to 10-bit screening frame (k=35, f=10). H0-H11are Hamming distances showing the number of disagreeing bits between twobinary vectors.

[0096] FIGS. 38A-38F are plots showing the correlation betweenimplausibility numbers and demographic parameters of 220 countries underanalysis. FIGS. 38A, 38C and 38E show the ln M values obtained with thereference object being Saudi Arabia. FIGS. 38B, 38D and 38F show the lnM values obtained with the reference object being Russia. The ln Mvalues were computed with the use of 34 demographic parameters. FIGS.38A and 38B demonstrate the ln M correlation with the percentage of themale populations of the age group of 00-04; FIGS. 38C and 38D, with malepopulations, age group of 20-24; and FIGS. 38E and 38F, with malepopulations, age group of 75-79.

[0097]FIG. 39 illustrates the climatic data analysis by the HyGV-methodand shows the relationship between the values of February normal dailymaximum temperatures (F.°) and multiplication numbers computed for 245cities and locations of 50 states the U.S.A., with Charleston, S.C., asa reference object. Dark dots correspond to 33 central, east coast andsome of southeast states: AL, AR, CT, D.C., CA, IA, IL, IN, KS, KY, MA,MD, ME, MI, MN, MO, NC, ND, NE, NH, NJ, NY, OH, PA, RI, SC, CD, TN, VA,VT, WI, and WV.

[0098]FIG. 40 is a flow diagram explaining the differences betweenintuition and reasoning.

[0099]FIG. 41 is an illustration of cluster trees showing the changes inthe way of clustering that occur upon the addition of 1's to a naturalsequence of numbers from 1 to 24.

[0100] FIGS. 42A-42F are illustrations of HyPa self-evolution induced byconsecutive addition of duplicates of analyzed objects to the capsule ofclones. The plots in FIGS. 42A-42F show the results of analysis ofpopulation pyramids (34 demographic parameters) of 94 countries,including 57 member-states of the Organization of Islamic Conference(indicated by open dots), 36 European countries with predominantlyChristian populations, as well as Israel, with predominantly Judaicpopulation, (dark dots). The capsule of clones was constructed usingFrance as a reference object. In all cases, the HyPa value was set toequal 1 [M(1)], except that in the analysis illustrated by FIG. 42A, themultiplication numbers, plotted on the ordinate, were computed at HyPavalue of 3 [M(3)]. In the analysis illustrated by FIG. 42A, all of theobjects (countries) were compared with the capsule of clones constructedfor object “France”. In FIGS. 42B-42F, duplicates of different objects(countries) were added to the same capsule of clones. In all cases, theduplicates were assigned the HyPa value of 1.

[0101]FIG. 43 is the illustration of the result of search for imageanalogs by emphasizing certain parameters and shows the locations ofanalogs of the query pose (see FIG. 21) after the “R-Toe” parameter wasemphasized by a 10-fold increase.

DETAILED DESCRIPTION OF THE INVENTION

[0102] 1. Introduction

[0103] 2. HyGV-method: generation and verification of hypotheses in theform of “hypothesis-parameters”. Information thyristor

[0104] 3. Infothyristor as a means for measurement of conventionalcomplexity

[0105] 4. Example of application of HyGV-method in clustering ofscattered data points

[0106] 5. HyGV-method in processing of climatic data

[0107] 6. HyGV-method in processing of demographic data

[0108] 7. HyGV-method in image recognition

[0109] 8. HyGV-method in gait recognition

[0110] 9. HyGV-method in sequence recognition

[0111] 10. HyGV-method in identification of target strings in binarysequences

[0112] 11. HyGV-method and mathematical statistics

[0113] 12. The interrelation between HyGV and ETSM methods

[0114] 13. Machine self-learning

[0115] 14. Conclusions

[0116] 1. Introduction

[0117]FIG. 1 shows a schematic diagram of the architecture of an enginethat provides basis for the preferred embodiments of this invention.This engine, under a conventional name of MeaningFinder™ (“MF”),represents a cooperative system wherein all of its elements are closelyinterrelated. The functioning of the individual modules of the MF systemis explained above in the section “Background of the Invention”. Inthose cases when it is clear which of the two metrics—R or XR—must beused for computation of a monomer similarity matrix, the MF systemprovides for fully unsupervised hierarchical clustering of objects understudy. In the method of this invention, the MF system works as aclustering detector, or information thyristor, that automatically andirrespectively of a data set volume identifies differences across ahierarchy of a community of objects under analysis.

[0118] The main concept underlying this invention is the establishing ofquantitative relationship between a hypothesis and facts. In thiscontext, a hypothesis is an idea—a priori existing or otherwisegenerated—on a possible relational organization of a given set ofobjects; and facts are a set of any number of variables (parameters)describing objects of a respective set of objects. In order to evaluatethe appropriateness and quality of an idea, we propose expressing it inthe form of a hypothesis-parameter, hereunder referred to as HyPa. Asthis invention may be applied to any set of quantitatively orsemi-quantitatively described objects or phenomena, for the illustrationpurpose we provide several examples pertaining to: clustering ofscattered data points, meteorological data processing, demographics,image recognition, and sequence recognition. Image recognition has beenchosen for visuality considerations, and also because a human body (andits various poses) represents a continuum, practically with no gaps andhiatuses, in which case the identification, classification and machinelearning are extremely difficult. As for demographics objects, theypresent interest based on many considerations: 1) input data that arecollected in a responsible and professional way represent what can bereferred to as a high-dimensional data space; 2) data are largelyavailable for public and research use; 3) the information presentsgeneral societal interest; 4) analysis results are a convincingdemonstration of a high commercial potential of the proposed method thatprovides solutions to various problems involving demographic specificsof particular localities; and 5) discoveries on demographic specifics ofpopulations of the world always lead to interesting associations(geographical, ethnographical, religious, economical, historical, etc.)which, in their turn, can be a source of heuristic ideas. In theexamples demonstrated below, we intentionally present average(non-optimized) results to emphasize the fact that what demonstratedhere is not some carefully selected and polished case studies fortextbooks or marketing presentations, but a real-life working methodavailable for a regular PC user. However, it was not the practical ortheoretical value criteria that determined the choice of the examplespresented in this disclosure. In the foregoing section on the backgroundof this invention, we have suggested that, certain conditions provided(algorithmic integrity, cooperativeness, and rational combination of thedeductive and inductive in the approach to data processing), a computerprogram can display the abilities of an expert with an individual“system of values” and individual perspective of objects and phenomena,i.e. can have an “ego”. Obviously, to demonstrate that this inventionprovides for reaching the above-formulated goal, it would not be enoughto provide a couple of examples. In order to prove that a certaincomputer program has its own “outlook”, one has to produce a specialkind of evidence of the achievement of the goal: as a minimum, a verywide range of objects of application. Also, as we will show in thefollowing description, the no less important point is the demonstration,on the examples of diverse problems, that a system does haveindividuality in “perception” and assessment of problems, just as humansdo.

[0119] 2. HyGV-Method: Generation and Verification of Hypotheses in theForm of “Hypothesis-Parameters”. Information Thyristor

[0120] A “hypothesis-parameter” (“HyPa”) is generated in one or anotherway and represents a digitally expressed idea on how objects orphenomena under analysis are interrelated. The principle ofdigitalization is simple: objects within an HyPa can be assigned suchdigital values which upon clustering of those objects based on the HyPaas the only parameter will provide the same result (a tree of clusters)as the one based on all available parameters in the absence of an HyPa.

[0121] Human assessment of similarities between objects or phenomena ismostly of planar nature, unlike, for instance, the way situations areassessed in chess analysis. Indeed, the assessment scale used by humansfor evaluation of objects and phenomena is not very detailed, and it hasits adequate verbal reflection in the form of evaluative words, such as:extremely poor, poor, satisfactory, good, very good, excellent, etc.Respectively, such an evaluation space can be presented on a scale, forinstance, from 0 to 5, where “extremely poor” corresponds to 0, and“excellent”, to 5. A narrow range and quasi-superficiality of such ascale are, in fact, very important for reasoning dynamics and for theoptimal ratio between induction and deduction. In the method of thisinvention, an HyPa is evaluated through ETMS-based clustering, and forthe consideration of the above-noted peculiarity of human assessments,clustering is done based, mainly, on two nodes (FIGS. 2A and 2B). HyPacan also be designed so that clustering of objects described by HyPa asa single parameter provides a multi-node similarity tree. For instance,the examples demonstrated in this and in the following sub-sections“Example of application of HyGV-method in clustering of scattered datapoints” and “Demographic data processing” show the clustering based onfour or more node trees. However, as demonstrated by various otherexamples presented in this disclosure, similarity on a two-node levelprovides both sufficient and optimal solution of extremely complextasks.

[0122] To verify a hypothesis, an HyPa, along with other parameters thatdescribe a given set of objects, is introduced into MF (FIG. 1) whichmust determine what number of HyPa multiplications (M) is enough toinhibit the effect of the rest of parameters and to produce a clusteringresult that would be the same as in the case when only one HyPa is usedinstead of all the parameters. The process of determining amultiplication number is illustrated in the block- diagram in FIG. 3.

[0123] Natural logarithm of number of HyPa multiplication, M, is theso-called implausibility number, ln M, representing a dissimilaritydegree; accordingly, −lnM is the plausibility number and represents asimilarity degree. If a hypothesis fully agrees with the totality of theavailable information (i.e. provided by all other parameters), M equals1, and the implausibility number equals 0. The lnM values of 5-6 orhigher indicate that a given hypothesis fails to reflect the reality ofa respective set of data; 2 to 3 corresponds to a “good” mark, and 1 to0 means a very high degree of correspondence (“very good” to“excellent”).

[0124] HyPa may be created: (a) for a single object (HyGV-CC-method);(b) for a group of objects (HyGV-GR-method) whose interrelations havebeen established through any kind of data analysis; and (c) based on apriori existing idea about objects' relationships in a given set ofobjects (HyGV-ID-method). A single-object HyPa is used when it isnecessary to locate analogs of a reference object in a set of unknownobjects. A group HyPa (GR) is used when some information on objectsunder analysis is available, e.g. a preliminary clustering result, andthe investigation purpose is to find out whether a classificationassumption is valid, or whether among the objects that have not beenanalyzed there may be others that are close to any of the establishedgroups, etc. The ID-method is applied when an analysis of a set ofobjects is approached from the standpoint of a sheer hypothesis based onvarious kinds of information, including experience, impression, andguessing. In practice, different HyPa methods may be combined.

[0125] To generate an HyPa of the first type (a single-object HyPa), aso-called “capsule of clones” (“CC”) of a reference object is created. Acapsule of clones is built of a number of artificially created,according to a certain deliberately chosen principle, objects-analogs ofa reference object, for instance, by changing—increasing or decreasing(or, e.g., alternating increase/decrease in even/odd cells)—all or partof a reference object's parameter values by a certain coefficient. Thuscreated clones of a reference object form a certain shell (capsule)within which the clones are numerically labeled. It can be assumed thatthe greater is the difference between the clones in a capsule, the wideris a capsule, and the higher is the probability that it contains objectsthat are too dissimilar to a target object. However, in reality itappears that a capsule's “width” is not as critical as it may seem tobe. This is due to a very high selectivity of the quantitativeassessment provided by the method of this invention, and because of thefact that formation of a capsule is only a part of the HyPa generationprocedure. This issue will be discussed in more details below in thesub-section “HyGV-method in processing of demographic data”.

[0126] The mechanism of performance of a capsule of clones can be bestexplained on the following example. Assume that we have an image of ahuman figure standing upright, facing the observer, legs are positionedstraight and parallel, arms are at the sides. In an ideal case, imagesof an identical and maximally similar poses could be located in theprocess of screening and sorting operations applied to a database ofvarious human body poses. Now assume that we have created a capsule ofclones of the reference image, which includes images that differ fromthe reference figure by the angle of the position of the right hand,varying from “down” to “up”. If we construct an HyPa covering the seriesof clones with different angles of the right hand position, the numberof images extracted from the database will expand due to variations inthe position of the right hand. A capsule of clones is a tool for makinga reference object less definitive and creating an “impression about thetarget object”. It provides a sort of associative bridge between analogsby making their capsules overlapping and thus imitating the elements ofassociative reasoning.

[0127] The next step in HyPa generation is the clustering anddigitalization of clones in a reference object's capsule. Since, asmentioned above, in most cases, we apply a 2-node clustering (FIGS. 2Aand 2B), the resulting grouping provides either three or foursubclusters, i.e. either only one of the two primary clusters undergoesfurther division into two subclusters, or each of the two primaryclusters is further subdivided into two subclusters. In the first case,it is aa, ab, and b, i.e. CC3(2) (see FIG. 2A), whereas in the secondcase, the four subclusters are designated as: aa, ab, ba, and bb, i.e.CC4(2), where “2” is a number of nodes in a capsule (see FIG. 2B). Inprinciple, a capsule may contain any number of clones, although inoptimum, 5 to 7 clones suffice to make an efficient capsule. As a rule,clones that belong to a same subcluster are marked with one and the samenumber (an HyPa value).

[0128] After a capsule of clones is constructed and digitalized, theso-called initial number of multiplications (M₀) should be establishedfor it. In case of a correctly designed HyPa, M₀ for a reference objectequals 1, i.e. the designed HyPa used as the only parameter produces thesame clustering as the totality of all other parameters of the objectscovered by the hypothesis-parameter. Although it is desirable that M₀=1,this is not a mandatory condition (below we will demonstrate that thenumber of multiplications represents an additive value wherein actualM-values are added to M₀-values). FIGS. 4A and 4B are illustrations oftrees resulting from clustering of C3(2) capsule of clones. The tree inFIG. 4A corresponds to clustering based on HyPa only; and the tree shownin FIG. 4B is a result of clustering based on the rest of parametersdescribing the clones within the capsule, excluding the HyPa. Despitethe differences in the branch lengths, the trees are identical asgraphs, and the M₀ value in this case is 1. FIGS. 4C and 4D show thecluster trees obtained, respectively, using the HyPa only and using allof the rest parameters (excluding the HyPa) but after adding the object“R” (i.e. a reference object in the likeness of which the capsule ofclones was created) to the capsule of clones. The “R” object was giventhe same value as subcluster “ba”. As is seen, the addition of thereference object “R”—based on which the capsule of clones wasconstructed—to the capsule of clones does not, in principle, change theclustering tree graph.

[0129]FIG. 5 illustrates a case when the HyPa multiplication numberappears to be higher than 1—i.e. when the trees—the one based on theHyPa only, and the one based on the use of all parameters—areincongruent. Blocks 501 and 502 show the clustering schemes foranalyses: with the use of all parameters, but not the HyPa, and with theuse of the HyPa only. Here, “T” is an object under analysis (i.e. atarget object), and “A_(R)” and “B_(R)” are, schematically, twofractions of the capsule of clones constructed based on the referenceobject “R”. The value assigned to “T”-object was the same as that ofsubcluster “A_(R)”. As is seen, unlike the case when only the HyPa isused as a basis for clustering (see block 502), the use of allparameters, except for HyPa, results in “T”-object being allocated in asubcluster apart from the “A_(R)” capsule of clones, even though bothhave the same value within the HyPa. Now, if we add a certain (“M”)number of HyPa extra copies to the parameters used in clustering,“T”-object will move to the same cluster as “A_(R)” (see block 504). The“M”-number depends on how different the objects “T” and “R” are. Thus,FIGS. 4 and 5 provide a visual demonstration of the principles thatunderlie the HyPa-method.

[0130] It is obvious that the above-described case demonstrates aparadox: the input of extra copies of absolutely identical information(information quanta) in addition to existing information results in theoutput of qualitatively new information—on the degree of similaritybetween “T” and “R” objects. The schemes shown in blocks 503 and 504 inFIG. 5 demonstrate that this invention is, in reality, theimplementation of the principle of thyristor in information technology.In this implementation, “GATE”, i.e. bifurcation point, separates“ANODE” (“T”-object) from “CATODE” (capsule of clones). When an adequatevolume of additional information stream in the form of extra copies ofan HyPa is supplied to “GATE”, it turns the “GATE” valve on, letting the“ANODE” information flow to “CATODE”, thus moving a “T”-object into acapsule of clones. By changing the HyPa format—i.e. changing theNBI-system's “ego”—we can regulate the volume of the information signalsent to “GATE” so that it is sufficient in order to turn the “GATE”valve on.

[0131] 3. Infothyristor as a Means for Measurement of ConventionalComplexity

[0132] Despite the seeming complexity of the implementation of theHyGV-method technology, its principle is, in fact, as simple as thethyristor principle. The discovery of the opportunity for practicalimplementation of the information thyristor (hereunder referred to as“infothyristor”, provides new perspectives of many aspects—including theleast explained—the functioning of brain cells. In particular, itprovides a method for information processing wherein information is usedfor processing information. The above-said indicates that theinfothyristor provides for measurement of conventional complexity ofobject “T” in relation to object “R” within the range from 1,corresponding to zero complexity, to infinity. When the measurement unitis expressed (by the operator or computer) in the form of an HyPa,giving it a certain qualitative essence, conditional complexity can bequantitatively and highly accurately measured by the infothyristor.

[0133] The role of complexity is paramount in the context of informationtechnology. One of the most widely cited criteria for informationassurance, along with Shannon's information entropy, is the Kolmogorovcomplexity criterion (Kolmogorov, A. N. 1965. Three approaches to thequantitative definition of information. Prob. Inform. Transmission, 1,4-7). According to Kolmogorov, complexity can be measured by the lengthof the shortest program for a universal Turing machine that canaccurately reproduce the observed data. However elegant, this approachto non-probabilistic description of complexity has a major flaw: theKolmogorov complexity cannot be computed, as “the shortest program”notion is only relative, and there is no way to establish with certaintywhether or not a particular program is indeed the possibly shortest one.Another problem with the Kolmogorov complexity as a criterion forcomplexity is the fact that a universal Turing machine, by definition,can have a variety of implementations, and each of them will have itsown “shortest” program. Certainly, one can go around thisproblem—mathematically—by assuming that complexities as per differentTuring machines should differ from each other by certain additiveconstants. Nonetheless, it is apparent that the practical applicabilityof the Kolmogorov complexity is seriously hampered by those two factors.

[0134] The above-said should help to appreciate the simplicity andrationality of the infothyristor proposed by this invention. Theinfothyristor, in pari causa, provides a signal, as good as conventionalcomplexity, however, devoid of the above indicated flaws of theKolomogorov complexity. Firstly, it allows for establishing a preciseminimal value of “information current” that is needed to be supplied tothe GATE in order to trigger its turning on. For simplicity andvisuality considerations, further in the description, we operate withmultiplication numbers computed at an accuracy of ±1; however, inreality, the threshold for the GATE switch-on can be reliablyestablished, in terms of the multiplication number, at an accuracy of upto the decimals and hundredths. A value of such threshold for a givenset of input data and a given HyPa does not depend on characteristics ofa computer. Secondly, one and the same HyPa allows for evaluation of arelationship between an indefinite number of objects by using a responsemeasured in the same dimensionality.

[0135] In fact, the above defined evaluation method can ideally servethe purpose of artificial imitation of the brain cognitive activity.Indeed, a combination of an artificial “ego”, in the form of an“implanted” HyPa, and information processing scheme of the type of theinfothyristor relieves the artificial (or non-biological) intelligencefrom the necessity of self-identification when performing the objectidentification job. The HyGV-method allows for discovery of information,pertaining to any set of objects, in a certain homogenous dimensionlessspace that ultimately facilitates the comparison of even most differentobjects to each other. Being able to create and utilize such a type of aunified information space is vitally important for any apparatusimitating the biological intelligence. In fact, it looks as if it mustbe important not only to apparatuses—in other words, it may well be theway the biological brain does information processing. The followingexamples, illustrating the work of the method of this invention,demonstrate that information processing by the HyGV-method displays acertain peculiarity that strongly resembles the human way of thinking.

[0136] 4. Example of Application of HyGV-Method in Clustering ofScattered Data Points

[0137]FIG. 6A illustrates the result of clustering, by the ETSM-method,of 33 scattered points described in X, Y, and Z coordinates. The appliedmetric is the XR-metric, at B=1.50 (Equation 5). As is seen, theclustering produces 6 groups of points at 6 different levels, marked asNo. 1 through 6: group No. 1 consists of one point; groups No.2-5, of 4points each; and group No. 6 includes 16 points. Based on such grouping,we construct a hypothesis-parameter of the GR-type, so that it includesall 33 points, and label each point within the HyPa in accordance withthe number of the group it belongs to, as shown in FIG. 6A. (The resultwill be the same if the labeling is done in a reverse order.) Theclustering based on the HyPa as the only parameter of the 33 pointsproduces the tree which is illustrated in FIG. 2C. When we compare theHyPa with the values of the X, Y and Z parameters of the whole set ofpoints, it appears that the M₀ value equals 1.

[0138] Now we will start changing the value of the X parameter of thepoint marked with an asterisk in FIG. 6A. In the course of such changes(in both lower and higher value directions), the number ofmultiplications, M, progressively increases. The plot in FIG. 7 showshow the value of the −ln M criterion (plausibility number) changesdepending on the deviation of the asterisked point's X parameter valuefrom its original value for which −ln M was 0. The curve reveals asymmetrical (i.e. corresponding to both increasing and decreasingchanges of the X value) monotonous dependence in the range from −1200%to 1200%.

[0139] A change of the asterisked point's X value by as little as ±0.5%makes group No. 3 break to two subclusters with 2 points in each.Further variations in the X parameter of the asterisked point causeconsiderable qualitative changes of the clustering picture, which getsstabilized starting with X±13% further remains in the form shown in FIG.6B. The asterisked point now forms a subcluster standing apart fromother 32 points, while the subcluster to which it previously belongedand which now consists of 3 points, instead of 4, still has the sameposition level in respect to other subclusters. The clustering resultshown in FIG. 6B represents the state of the grouping when the value ofthe X parameter of the asterisked point was decreased by 500%.

[0140] The above-presented example demonstrates very important specificsof the HyGV-method: in this method, clustering is a sort of detector ofconflict (whose level is reflected by M values) between ahypothesis-parameter and actual facts contained in a set of variables(parameters) describing the set of objects. The response provided bythat detector is continuous, unlike clustering where any changes resultin discontinuity. Even though the signal changes in response to themagnitude of deviation from the initial X-coordinate of the asteriskedpoint is close to “sigmoidal” or hyperbolic tangent function, speakingin artificial neural network technology language, we are not inclined touse any biological analogies or to draw a parallel with the synapticsignal. However, it would be hard not to notice that the character ofthe response variations clearly reveals a highly cooperative nature ofthe system. Indeed, the above discussed example demonstrates twoprincipally different states: (1) the asterisked point being an organicpart of the system of 33 points (FIG. 6A), and (2) the same pointrepresenting an autonomous detached cluster, not bound to the system ofclusters of the other 32 points (FIG. 6B). This sharp “conformational”switch from a strong-binding state to a weak-binding state, as well asthe fact that even after the separation of the asterisked point, thesystem responds in accordance with a distance between that point and thegroup of the rest of the points are the evidences of a highcooperativity of the software interface.

[0141] 5. HyGV-Method in Processing of Climatic Data

[0142] This second example of application of the HyGV-method deals witha search for analogs with similar climatic characteristics. The data setincludes the following 108 climatic characteristics of 245 cities orlocations (such as airports and counties) of 50 states of the U.S.A.(all data are based on multi-year records through 2000): morning andafternoon values of relative humidity, in per cent, for each month ofthe year (the total of 24 parameters), relative cloudiness, in percent,based on multi-year average percentage of clear, partly cloudy andcloudy days per month (the total of 36 parameters), normal daily mean,minimum, and maximum temperatures in degrees of Fahrenheit (the total of36 parameters), as well as normal monthly precipitation, in inches (thetotal of 12 parameters). The comparative climatic data are availablefrom National Climatic Data Center: http://lwf.ncdc.noaa.gov. Theobjective of this example is to demonstrate that the HGV-method can beused for differentiation of climate patterns not only in the north/southdirection but also in the west/east direction, hence for establishingthe divergence across the climatic gradient, which is of high practicalvalue. For this purpose, we have chosen two cities located on the samelatitude: San Diego, Calif., on the west; and Charleston, S.C., on theeast. HyPa was constructed by the CC-method: CC4(2) capsules of 8 cloneswere created for each of the two target cities. HyPa consisted of thefollowing values: aa(1)=1; ab(3)=15; ba(1)=30; and bb(2)=60. The numbersin brackets indicate the clone numbers in the subclusters. The metricused was XR with B=1.50.

[0143]FIG. 8 shows the relationship between the ln M_(ab) and ln M_(ba)for San Diego, Calif., demonstrating that the closest climatic analogsof San Diego, Calif., are Los Angeles AP (LAX International Airport),Calif., and Long Beach, Calif. All other 242 cities/locations in thedatabase have significantly less similarities with San Diego. It isnoteworthy that among those 242 cities/locations, many are located onthe oceanside and have a warm climate—including Charleston, S.C.,located on the latitude as San Diego and coastal cities in GA, LA, FL,TX, and HI. However, the HyGV-system has selected as San Diego analogsonly those that are located on the U.S. southwest Pacific shore. EvenLos Angeles County has been identified as a remote analog of San Diego(as it includes cities that are much farther from the oceanside than LosAngeles AP is).

[0144]FIG. 9 shows the relationship, for 243 U.S. locations, between theln M_(ab) values, established using San Diego, Calif., as a referenceobject, and the ln M_(ab) values established with Charleston, S.C.,being a reference object. First of all, it indicates that the closest(climatic) analogs of Charleston, S.C., are Savannah, Ga., andJacksonville, Fla.,—both located on the East Coast but slightly fartherto the south than Charleston. Secondly, it is evident that the datapoints on the plot are arranged in the form of a Y-shaped bifurcation:the left-side branch of the “fork” includes the points corresponding tothe East Coast and Southeast cities; the right-side branch correspondsto the West Coast cities; and the group of points on the line prior tothe ramification includes the cities located in Alaska, northern states,and in the arid zone, such as Arizona and Nevada. The first group(inside the dotted-line rectangular, for visualization purposes)includes the cities of the following states (the first numbers inparentheses indicate numbers of cities of a given state that appeared tobe in this group, while the second numbers show total numbers of citiesof a given state whose climatic data were processed): AL(4/4), AR(2/2),FL(10/11), GA(6/6), LA(4/4), MS(3/3), NC(6/6), OK(2/2), SC(3/3),TN(4/5), TX(13/17), and VA(2/4). If we further take into considerationthat the only FL location that the search engine has excluded from thisgroup is Key West, located half-way between Cuba and the southern edgeof the Florida peninsula, and that the four TX cities excluded from thesame group (Amarillo, El Paso, Lubbock and Midland-Odessa) are locatedon the border with, or close to, New Mexico and have a close to aridclimate, it becomes obvious that the HyGV-method is amazingly effectiveas a tool for highly selective differentiation between the climaticconditions of the U.S. west and east.

[0145] The above-discussed study has demonstrated that the appliedHyGV-CC-method allows for finding analogs of any of the 245 cities inthe database, and in all of the cases, the analog identification washighly intelligent and accurately reflected the geographic positions,hence climatic peculiarities, of the cities. It is also extremelyimportant that the HyGV-CC method does not need “training”. It receivesand processes high-volume, distributed and complex data and analyzesthem in the same way as a human expert would do.

[0146] 6. HyGV-Method in Processing of Demographic Data

[0147] The GR-method of HyPa generation is demonstrated below by theexample of processing of demographic data. Earlier (copendingapplication by L. Andreev “High-dimensional data clustering with the useof hybrid similarity matrices”), we showed the ETSM-processing ofdemographic data on 80 countries, including 41 countries withpredominantly Muslim populations; 38 European countries withpredominantly Christian populations; and Israel, with the predominantlyJudaic population. Each of the countries was described by 51 demographicparameters according to data for the year 2000 (U.S. Census Bureau,International Data Base, IDB Summary Demographic Data, by John Q. Publichttp://www.census.gov/ipc/www/idbsum.html) including population pyramidsections (total of 34 parameters, reflecting percentages of each agegroup in the total male and female populations, respectively), birth anddeath rates, life expectancy at birth, infant deaths, fertility factor,male/female ratios (total of 6 parameters), and dynamics of populationgrowth in various years compared to the year 2000 (total of 11parameters). All monomer matrices were computed with the use of theR-metric. It was demonstrated that the unsupervised clustering resultedin formation of 4 distinct groups of countries, with no outliers: 1)countries with predominantly Muslim populations; 2) Israel; 3)capitalist European countries with predominantly Christian populations(17 countries); and 4) former socialist European countries withpredominantly Christian populations (21 countries). Based on theseclustering results, the HyPa was formed on a 4-node basis and including5 subclusters (C5(4)):

[0148] a=5 (Egypt, Kuwait, Morocco, Saudi Arabia);

[0149] ba=4 (Israel);

[0150] bbaa=3 (Bulgaria, Latvia);

[0151] bbab=2 (Croatia, Czech Republic);

[0152] bbb=1 (the Netherlands, Norway, Sweden, UK).

[0153] This example is not intended to draw any scientific conclusionson demographic peculiarities of the said countries, but to demonstratemore complex than C3(2) and C4(2) schemes (FIG. 2C).

[0154] The group method HyPa has high plasticity, which is especiallyimportant in the extraction of information from large data sets. As isseen in FIG. 10, division of the former socialist countries into twogroups provided a compact character of the cluster of capitalistcountries (B) located on the X-Y plane of the 3D-diagram. All countrieswith predominantly Muslim populations distinctly stand among all othercountries and are located in the Y-Z plane of the 3D-diagram.

[0155] We will now demonstrate the performance of the third of the abovedescribed HyPa generation methods (the ID-method). Assume that there isno any kind of preliminary information on how the 80 countries can begrouped based on their demographic characteristics. As is known fromhistory, Judaism emerged as a religion much earlier than Christianityand Islam, and that the latter is a relatively young religion, with thehistory of certain antagonism to Christianity. Let us then assume thaton a certain scale Judaism occupies the median position, withChristianity and Islam on two opposite sides. Let us also assume thatformer socialist countries with predominantly Christian populations,having been exposed to certain long-term effects of the socialistsystem, must be somewhat different from the rest of the countries withpredominantly Christian populations. To create an HyPa, let us take fourcountries that are historically associated with the formation anddevelopment of the three religions: Israel, Saudi Arabia, Italy, andRussia. Digitalization by the C3(2) scheme provides the followingnumerical values of HyPa:

[0156] a=12 (Saudi Arabia);

[0157] ba=5 (Israel);

[0158] bb=2-3 (2 for Russia, 3 for Italy).

[0159] As was the case with the previous example from demographic dataprocessing (GR-method), this example does not claim to be any sort ofscientific conclusions in regional studies—this is purely ademonstration of a technique for generation of the idea-based HyPa.

[0160]FIG. 11 shows a 3D-diagram of distribution of the 80 countriesbased on demographic characteristics (51 parameters) and by applying anidea-based HyPa. As is seen, the clustering gives straightforwarddistinction between Christian, Islamic and Judaic countries, and furtherdivides the groups of Islamic and former socialist Christian countriesinto two subgroups each—A-B and D-E, respectively. Thus, we haveobtained six homogenous subclusters by applying the HyGV-methodinvolving an idea-based HyPa.

[0161] As far as clusters A, C, D, and the allocation of Israel areconcerned, they demonstrate an ideal accord between the underlying dataand the generated HyPa. 11 countries (14% of all countries underanalysis) fall into subgroups B and E, which obviously points to theircertain differences from the respective main clusters. For aprofessional researcher in demographics, these may well appear asexceptions that confirm the rule. For instance, the 6 countries ofsubgroup B differ from the rest 35 countries with predominantly Muslimpopulation only according to X ordinate reflecting the similarity withSaudi Arabia. The subgroup includes, for instance, Lebanon, Kazakhstan,and Azerbaijan—with multinational and multi-confessional populations;Albania, which as well as Azerbaijan and Kazakhstan, is a formersocialist country; and Turkey, with its distinctly secular state system.Subgroup E includes 5 former socialist countries that stand apart fromthe rest of the former Soviet bloc countries by relatively high GDP percapita.

[0162] Thus, the results shown in FIG. 11 demonstrate that the method ofthis invention allows for screening of right and false ideas, and whenan idea indeed reflects the relationships between objects in a givendataset, the method provides an insightful clustering based on thatidea. Here, the idea serves as a basis, if not for concrete values ofHyPa, but at least for a general view of quantitative differencesbetween the objects within HyPa. Further, an idea-based HyPa may be usedfor a wide variety of objects that are not directly associated with theunderlying idea.

[0163] The fact that ethical and social relationships, hence demographicvariables, in populations of different countries are greatly influencedby a dominating religion is by and large a common knowledge. What thisexample technically demonstrates is that the HyGV-method, havinganalyzed the proposed idea, produced an exceptionally clear-cutclustering result that would not be possible to obtain by any otherheretofore available methods. The non-randomness of the obtaineddistribution is further supported, for example, by a compact grouping ofthe countries with mostly Chinese population, which is shown in FIG. 12(where groups B, D, and E, shown in FIG. 11 above, are removed forsimplification purpose). Even more distinct picture of the grouping of 5states with predominantly Chinese populations is illustrated by FIG. 13.Here, the clustering by the ETMS-method using the R-metric was performedwith the use of four different multiplication numbers as parameters;multiplication numbers were computed based on HyPa values of 2, 3, 5,and 12, using the above-described HyGV-ID method (FIGS. 11, 12). This isthe case when an idea (based on which the hypothesis-parameters weregenerated) was applied with the purpose of reducing 51 parameters to 4parameters. This example demonstrates the possibility of concept fusion.Any large number of heterogenic parameters corresponding to a diverseset of objects' properties can be subdivided into more homogenous groupsof parameters, each group joining together related categories ofproperties. Then, an HyPa is to be modeled for each of such groups, andmultiplication numbers obtained based on each of hypothesis willrepresent a final set of parameters to be used in clustering of theobjects under analysis. All three types of HyPa can be used in oneanalysis.

[0164] Thus, the foregoing demonstrates, on real-life data, all of thethree techniques for HyPa generation and application: CC, GP, and ID.The first one can be used as a routine method for extraction ofinformation from large datasets—or, what computer science referred to as“intelligent data understanding”, “data mining”, knowledge discovery”,etc. One of the strongest sides of the HyGV-CC method is its “naturalintelligence”: it requires neither training, nor supervision, nor aspecially trained operator. This aspect of the method was displayed inthe above-shown examples and will be further demonstrated in thefollowing parts of this disclosure.

[0165] Among the three proposed methods for HyPA generation, the methodbased on construction and use of capsules of clones (HyGV-CC method) isthe most efficient in automated unsupervised data processing. Itprovides the basis for the new, non-probabilistic statistics which, infact, represents the essence of this invention. This new statisticsprovides conclusive decisions on similarities and dissimilarities evenwhen there are only two objects under comparison; and not only may anumber of parameters describing the objects under analysis be unlimited,but the fact is that however many parameters there may be involved,their number does not affect the computation cost. The clusteringdetector (information thyristor) that provides the main instrument inthis new statistics is algorithmically simple, and the whole technologyis implemented on a regular PC.

[0166] It may look as if the capsule of clones plays the same role anull hypothesis allowing for evaluation of an alternative hypothesisrepresented by target objects. However, in principle, this is not so. Asis shown in the section “Background of this invention”, the clusteringdetector provided by this invention represents in its essence a resultof the process of data averaging, occurring in two opposite directions:putting together (objects in clusters) and division (of objects intoclusters). Thus, data processing by ETSM-method, providing also forsmoothing and averaging of random deviations, is by itself a statisticalprocessing in the generally accepted meaning of statistics. Thefollowing example, illustrated by FIG. 14, demonstrates that differencesin composition of a capsule of clones do not significantly affect afinal result of data processing. This case study deals with data on 17states with predominantly Muslim populations, described by 34 parametersbased on population pyramids. Capsules of clones were constructed sothat each next clone differed from a preceding one by a same value asthe first clone differed from the reference object. The capsules ofclones were made for Saudi Arabia; each capsule consisted of 10 clones,and each next clone differed from each preceding one by 0.4% and 1.6%,respectively (indicated by the open and dark dots, respectively, in FIG.14)—hence, the last clones in each capsule differed from the respectivereference objects by 4% and 16%, respectively. For clustering purposes,five clones with order numbers 1, 2, 5, 9, and 10 were selected in eachcapsule, and the clustering with the use of R-metric produced trees ofthe C3(2) format (cf. FIG. 2A) HyPa was digitalized as follows: a(clones 9, 10)=10, ba (clone 5)=2, and bb (clones (1, 2)=1.Multiplication number for 17 states was computed in two ways: HyPa valuefor each of the objects under analysis being: 2, in which case,multiplication number is denoted as M(2); and 3, respectively, withmultiplication number of M(3).

[0167]FIG. 14 shows the relationships between M(2) and M(3) for all 17states. As is seen, neither the four-fold broadening of the capsule“width” (i.e. the difference between parameter values of the referenceobjects and their tenth clones), nor the values assigned to the objectin the HyPa affect the appearance of the linear relationship. Theconstruction of a capsule of clones being so simple, it is obvious thatthis process can be standardized and automated, even more so that inmany cases, even for large databases comprehensive analysis, it sufficesto create a capsule clone for one or a few reference objects. Even ifdifferent analysts use different capsules of clones based on randomlychosen parameter values for one and the same set of objects, theirresults will show a good correlation.

[0168] The most valuable feature of the HyGV-method is nonlinear,non-planar logic that underlies analysis and decisions. A space ofsimilarities between complex objects (i.e. objects described by numerousfeatures/parameters) represents a vector space. For instance, A is verysimilar to B, B is very similar to C, C is very similar to D, but D hasnothing in common with A. That is the way things are in the real world.FIGS. 15A-15F illustrates several simple examples demonstrating that theHyGV-CC method displays by far more complex logic than the logic basedon simple arithmetic (details of construction of the capsule of clonesare provided in Brief Description to the Drawings). For instance, inresponse to a query on analogs of Iraq, the HyGV-system selects theneighboring Syria, out of all other 40 countries with predominantlyMuslim populations (FIG. 15A). A query on closest analogs of Syriaprovides, along with Iraq, the neighboring Jordan, as well as Libya andAlgeria (FIG. 15B). A query on Jordan's analogs gives the neighboringSyria and Libya, but not Iraq. A similar situation is observed in thenext example: a query on Kuwait's analogs gives Bahrain, Brunei, andQatar (FIG. 15D); a query on Qatar's analogs gives United Arab Emirates,Bahrain, and Kuwait (FIG. 15E); but as for United Arab Emirates (FIG.15F), its analogs include neither Brunei, nor Kuwait or Bahrain.

[0169] Summing up the demonstration of the performance of HyGV-method indemographics analysis, we would like to point to the intelligent andnon-mechanical approach to the data under analysis, as shown in theexample illustrated by FIGS. 15A-15F. The same exceptionally highselectivity in information extraction was demonstrated in all cases ofdata processing by the HyGV-method. FIG. 16, further demonstrating theabove-stated, shows a 2D-plot of in M values, obtained in case of queryon Russia's analogs, as a function of ln M values in a query on SaudiArabia. The database for analysis included 220 countries, each describedby 34 parameters based on respective population pyramids. As is seen,for Saudi Arabia the closest, although not ideally close analog,selected out of the 220 countries, is the neighboring Oman. In case ofquery on Russia, the search for analogs gave Belarus and Ukraine, bothof which are former republics of the USSR and therefore, as well as formany other reasons, including historical, ethnical, religious, and otherfactors, have much in common in terms of demographics. On the plot shownin FIG. 16, all 13 countries located close to Russia (the dark dots) areeither former USSR republics or countries of the former Soviet bloc.

[0170] 7. HyGV-Method in Image Recognition

[0171] The practical application potential of HyGV-CC method,representing a search engine that allows, along with query processing,finding analogs of a query object, is relevant for a lot of differentareas—due to simplicity of both technical implementation of the methodand its task-specific modifications. In this and the followingsub-section, we will present the examples demonstrating the use ofHyGV-CC method in image recognition: one of them dealing withidentification of human poses; and another one, with gait recognition.Both of these applications are extremely important issues of computervision, and, particularly, robotic vision.

[0172] In image recognition tasks, the achievement of an analysisobjective is closely connected with an object parametrization problem.The latter may be approached from various positions, includinggeneralized technologies for object contour parametrization, and theoverview of those methods is not relevant in the context of thisdisclosure. In the presented example (FIG. 17), we have used one ofsimplest approaches: establishing the vertical distances between 18deliberately selected body points and a head top, assumed as a zeropoint. Capsules of clones were constructed as C3(2) and digitalized asfollows: a(1)=1, aa(2)=5, ab(2)=10. The XR-metric (B=1.50) was applied.

[0173] The image database analyzed in this example contained 75artificially generated images of human body poses. The images that arenot shown in FIGS. 19-24, are illustrated above in FIG. 18. The 75images cover a wide range of human poses, including: lying face down,lying supine, sitting, squatting, bent, poses with various positions oflimbs, etc.

[0174] FIGS. 19-24 illustrate the plots of ln Mb vs. ln Maa, each ofthem displaying data points corresponding to each of the 75 images. Theplots demonstrate how accurately the closest analogs of query imageswere identified. For instance, the search illustrated in FIG. 19 wasaimed at locating the closest analogs of a query image depicting a humanfigure with its back bent forward at almost right angle and with armsstretched forward, with palms touching each other. The result of thesearch shown in FIG. 20 further demonstrates high sensibility of theHyGV-CC method: it detects changes in position of hands. FIG. 21 showsthat the method allows for differentiation of figures in similar stancesbut with arms or legs stretched or bent at different angles. In thesearch illustrated by FIG. 22, the query was a figure with legs straightand hands down: as is seen the search for analogs has provided severalimages that are similar to the query image. FIG. 23 shows a search for afigure in a sitting posture. FIG. 24 illustrates a search for analogs ofthe image of a human figure lying on its stomach, hands under its chin:the obtained result points to several figures as close analogs, and animage of a figure lying on its back as most remotely similar. As wasnoted above, the search for analogs was performed within one and thesame database of 75 images of human figures in different poses.

[0175] As is seen from the above examples, the HyGV-CC method providesfor image recognition with exceptionally high selectivity. It does notrequire the “system training” by compiling analog files, etc.Metaphorically speaking, this is a “sniff search”—all that the searchsystem needs to “know” is characteristics of a query object, and it doesnot need “how to” instructions and commands. Database extraction ofreference object analogs based on similarity degrees can also be done byusing a criterion that is more demonstrative than the implausibilitynumber—namely, with the help of a so-called internal standardrepresented by an object maximally different from a query object, or anobject that embodies the limits of acceptable dissimilarities. In thefirst case, similarity degrees will vary within a range from 0 to 100%,whereas in the second case, similarity degrees may have both positiveand negative values. Similarity coefficient S_(i) is computed by asimple equation: $\begin{matrix}{{S_{i} = \frac{{\ln \quad M_{st}} - {\ln \quad M_{i}}}{\ln \quad M_{st}}},\%,} & (6)\end{matrix}$

[0176] where ln M_(st) and ln M_(i) are implausibility numbers of aninternal standard and of i-object, respectively. A plot in FIG. 25 showsthe relationship between S_(b(i)) and S_(aa(i)) similarity coefficients,wherein an internal standard was represented by a query figure in thesearch shown in FIG. 22. This is both highly illustrative andconventional way of visualization of HyGV analysis results. Forinstance, FIG. 25 illustrates the selection of query analogs with noless than 70% similarity. The use of internal standards allows fornormalization of the search for reference objects so that when newobjects that may strongly differ from a reference object are added intoa database, the positive section of the plot will not change. In thiscontext, it is important to note that the use of the XR-metric (theshape-metric), explained in the Background of the Invention, providesfor exceptionally high scalability in image recognition according to themethod of this invention, and, therefore, analysis of objects whosemeasurements are taken in real-life conditions at various distances froman object will provide absolutely same results.

[0177] As was previously mentioned, the multiformity of human bodyposes, being a challenge for image recognition, is a good test ofpractical usability of any method for image recognition. The methodologyfor recognition of various poses of human-like figures with four limbsand the trunk, presented in this disclosure, can be used as the basisfor autonomous machine-learning. Assume that we have created a system offiles on various human body poses, where each file contains an image ofa certain pose, labeled accordingly (expert assessment) and providedwith a detailed description of a respective pose. By setting limitvalues of Implausibility Number or of a similarity degree, we can useautomated screening of databases to find new analogs of poses labeledand stored in the created file system. (The fact that such a screeningwill not let through random objects has been proven by the examplesillustrated by FIGS. 19-24. The identification was intentionally donewith the use of just one coordinate—to demonstrate the screeningaccuracy.) If the limits are set to a higher level, the number ofanalogs added to individual files will increase, and certain analogimages may get attributed to more than one file, which is however not aproblem, especially since data extraction from files also can beregulated by setting required similarity level. Assume that thuscompiled and organized a database is used in identification of humanbody poses. It may appear that a certain experimentally measured imagedoes not have any match in an existing database, in which case a newfile is automatically created, however without a label and imagedescription. Such files are then labeled by a human operator accordingto their contents. Thus organized semi-automated system of machineself-learning will provide for search for images, whose analogs withinthe required limits of similarity are not present in the system'sreference library, as well as for expanding the latter by creating newimage files, which are separated from theretofore existing, familiar tothe system, files.

[0178] The proposed methodology of image recognition has certainpeculiarities that make it promising as a platform for development offully automated machine self-learning. The said peculiarities resultfrom the additivity of parameter multiplication. HyPa multiplicationnumbers strictly depend on certain factors. First of all, a number ofmultiplications required for a whole set of parameters is a sum ofmultiplications numbers for individual parameters. (This is totallyvalid, at least, upon the use of the XR-metric.) Individual parametermultiplication number can be established in two ways: either based on asingle parameter, or based on difference between a number ofmultiplications for a whole set of parameters and that for a whole setwith a target parameter removed. Both ways of computation give sameresults (as in the above examples multiplication numbers are computedwith accuracy to nearest whole number, the differences may be in therange of ±1). The above said is demonstrated in FIG. 26 in the form of atable of data on the first 14 human body poses illustrated in FIG. 18 (acapsule of clones was computed for the image used as a query in theexample illustrated in FIG. 21). Out of 18 points, used as parameters(see FIG. 17) of the artificially generated human-body images, 7referred to the left half of the body, 7 to the right half, and 4 to thetorso center. As is seen from the table, numbers of multiplicationscomputed for whole body parameters are practically the same as thetotals of multiplication numbers for the parameters corresponding to theleft and right halves and the torso center.

[0179] The additivity of parameter multiplication numbers provides veryimportant opportunities in the problem of computer self-learning. It canbe used for determining whether or not an image is symmetrical, for“configuring” an image, for instance, by combining different parts ofdifferent images, etc. Based on additivity, simple algorithms canprovide for detailed list of differences between a given image andreference images in a database.

[0180] Machine self-learning in recognition of a human-being's orhuman-like robot's poses will be beneficial for a multitude of practicalapplications. For instance, U.S. Pat. No. 6,493,606 “Articulated robotand method of controlling the motion of the same” by Saijo et al.describes a legged mobile robot that communicates by moving its limbsand/or the trunk “so that even a robot or a human being which does notpossess the same motion language database can determine the meaning . .. ”; such a robot may be used, for instance, for sending messages on thecondition of a dangerous working area. Our invention can significantlyenhance the mobile robot technologies, especially in the part of motionrecognition and remote control of robots by robots. It also opens newopportunities in the technology of robot pets—in both controlling arobot-pet by a human-being, and training a robot-pet to recognize themotion language of a human-being. The aforementioned examples cover onlya very small part of the opportunities in practical applications of theimage recognition based on the HyGV-CC method.

[0181] 8. HyGV-Method in Gait Recognition

[0182] As demonstrated in the foregoing sub-section of this disclosure,the method of this invention has unlimited potentials in pattern/imagerecognition, especially taking into consideration the high scalabilityand additivity of the total signal (number of hypothesis-parametermultiplications) that represents the total of signals contributed byindividual parameters. All of the above and especially the latter makethe HyGV-method a very promising solution for various areas of identityverification covered by the notion of “biometrics” (see e.g. Nanavati,S., Thieme, M., and Nanavati, R. (2002) Biometrics. IdentityVerification in a Networked World. John Wiley & Sons, Inc. New York;Cunado, D., Nixon, M. S., and Carter, J. N. (2003) Automatic Extractionand Description of Human Gait Models for Recognition Purposes. ComputerVision and Image Understanding, 90(1):1-41.) Biometric technologies—bothwell elaborated and widely available (for instance, dactyloscopy), andthose currently under development—carry a lot of commercial potential.One of the many new directions in biometric technologies is “gaitrecognition”, i.e. recognition of rhythmic patterns associated withwalking stride. Below we will demonstrate the use of the HyGV-method ingait recognition.

[0183]FIG. 27 shows 45 artificially generated schematic images(“frames”) of a human figure captured at various moments of the processof walking. The first frame was used as an object for computation of thecapsule of clones with the purpose of identification of other frames.FIG. 28 illustrates the motion dynamics determined by the HyGV-CCmethod. Parameter measurements were based on vertical distances from thehead top (assumed as 0) to 15 different points of the body. Body pointswere the same as in the example of image recognition discussed in theabove sub-section, except for the three spine points. The capsule formatwas C3(2) (FIG. 2A); a(2)=30, ba(2)=0.10, bb(2)=0.01. The motiondynamics computed based on all the parameters of 15 points is presentedby the two peaks of curve A in FIG. 28. Motion dynamics computedindividually for the left and right sides of the body, based on 7 pointsof a respective side plus the torso top, represent two curves, each withtwo peaks, however, with different peak maximums (see FIG. 28 curves Cand D, respectively, for the left and right halves of the body). Curve Bis the total of curves C and D and is practically identical to the curvebased on all 15 points, thus once again providing the evidence of theadditivity of the HyPa multiplication number.

[0184] As is seen from FIG. 28, even a very small fragment of a walkingstride can be used as a reference object for comparison and it is enoughfor the HyGV-method to establish a gait signature. Although the presentexample is based on artificially generated sequence of images of thewalking motion, it demonstrates the principle and the capabilities ofthe proposed method to be applied to the real-life human gaitrecognition. The described approach offers also new opportunities inestablishing gait specifics. In the copending patent application“High-dimensional data clustering with the use of hybrid similaritymatrices”, we described a technique for parameter multiplication in theprocess of hybridization of monomer similarity matrices. The parametermultiplication technique allows for adding, to a set of monomersimilarity matrices, any number of monomer similarity matricescorresponding to any parameter, which, in the context of gait analysis,can be used to establish which of the points of the body are responsiblefor the specifics of particular elements of gait dynamics.

[0185]FIG. 29 shows the walking motion dynamics established by using 9parameters based on the locations of 9 points of the body lower part(curve A). Curve B reflects the dynamics computed with the use ofadditional 4 copies of the parameter based on the left calf As is seen,the height of the first peak of curve B has increased 1.5 times ascompared to the first peak of curve A, whereas its second peak has notchanged—which means that the first peak corresponds to the motion of theleft side of the body, while the second peak reflects the motion of theright side of the body. This is further confirmed by the shape of curveC (FIG. 29) that was obtained after the addition of 4 copies of theparameter based on the right calf In this case, in contrast to theresult of the left calf parameter multiplication, the height of thesecond peak increased as compared to the second peak of curve A. In thesame way, by applying the parameter multiplication technique inreal-time, the gait-relevant specifics of the motion of any part of thebody can be established. After a general analysis and classification ofmotion dynamics of various parts of the body during walking measured byradar or video systems, individual parameter multiplication uponre-processing of the resulting data will provide further detailedsubgrouping of parameters and point to fine specifics of a gaitsignature under analysis.

[0186] 9. HyGV-Method in Sequence Recognition

[0187] Sequence recognition is involved in solution of many practicallyimportant problems, starting with document processing technologies,biopolymer analysis, and up to search for specific sections of spectrain engineering and biomedical research and applications. Below we willdemonstrate that the method of this invention allows for recognition ofany kinds of sequences and ensures exceptional accuracy as well asreproducibility, while being, unlike the neural network approach, verysimple and fully operational on a regular PC.

[0188] Assume that a certain sequence of elements from 1 to “n” withvaried signal values and containing an f-length string from “k” to“k+f−1” needs to be located. The query string can be presented as anobject described by k- parameters.(“reference object”) as is shown inthe Table in FIG. 30. Parameters 1 through f shown in the Table reflectthe signal values for each element of the sequence from the first to then-th. A capsule of clones (CC) created for the query string (“k” through“k+f−1”) will serve as a screening frame whose width covers ‘f’elements, and, while moving along the entire sequence, will be comparedto each object emerging on its way, thus leading to identification of aquery string. At each new step of the screening frame, there emerges anew object to compare with the capsule of clones of the referenceobject—with the same (f) number of parameters (i.e. same frame size) asthe reference object, but of different values. Each object exposed tothe screening frame is examined for the Implausibility Number. An objectwhose Implausibility Number is found to be zero is the reference object.

[0189] The following example is to demonstrate the efficiency of theHyGV-CC method in sequence recognition. An artificially generatedpattern consisting of 300 time-points (FIG. 31) with the signal varyingwithin a range of +100 to −100 units was used for analysis. Referenceobjects were two 20-units sequences: 40 to 59 and 80 to 99 time-points,respectively (k=40 and 80; f=20). Capsule of clones format: C3(2),a(1)=10, ba(1)=5, bb(2)=1. Objects were compared against cluster ba.Sequence 40-59 appeared to be the imitation of “white noise”, whereassequence 80-99 was a distinct block of signals, different frombackground noise. FIGS. 32 and 33 illustrate the detection of the querysequences at the points where the signal (the ln M value) slumps to zeroindicating that a reference and a target sequences coincide.

[0190] As is seen in FIGS. 32 and 33, there is a certain backgroundsignal that is characteristic for both the query sequences and the wholepattern. We will designate it as ln M_(0(i)), where i is a querysequence. For the above-referred query sequences, its values will be: lnM₀₍₄₀₋₅₉₎=4.0±0.5 and ln M₀₍₈₀₋₉₉₎=5.5±0.2. Based on the backgroundsignal value, the effect of uncertainty in respect of individualparameters upon the accuracy of locating a whole query sequence can becalculated. If uncertainty (U) is represented as follows:

U _(i)=ln M _(i)/ln M ₀, %   (7),

[0191] then it is easy to assess how the uncertainty in regard of someof the elements of a query sequence may affect the accuracy of itsdetection. The above thesis is illustrated in FIG. 34 showing how theuncertainty of locating the sequence 80-99 changes due to fluctuationsof the signal for element 83: as is seen, with the element 83 signalchanging by ±100%, U₍₈₀₋₉₉₎ does not exceed 50%. This technique forestimation of sequence detection accuracy depending on reliability ofavailable information on a given sequence can be used in analysis of anykinds of sequences.

[0192] The proposed approach to sequence analysis by applying theHyGV-CC method provides a universal solution for a variety of tasksinvolving sequence and pattern recognition. The foregoing description ofthe proposed methodology shows that the longer is a query sequence, thehigher is the accuracy of its recognition. Another important advantageof this method as a sequence (pattern) recognition technology is in itsintegrative function. Along with locating a reference section of apattern, the HyGV-method provides an integral response when it comparesa reference section with an entire pattern. This is well seen in FIG. 32where the reference object is a part of the spectrum (FIG. 31), verymuch looking like a “white noise”. The two broadened peaks extendingabove the signal's centerline (FIG. 32) are the integral form ofrepresentation of the analyzed spectrum's sections that distinctlydiffer from the noise signal.

[0193] The above-described application of the HyGV-method enables one toeasily locate, for instance, lengthy excerpts of texts in voluminousdatabases of documents, by applying a set of approaches: for instance,by establishing the number of words between two words that start with acertain letter, or the number of words in a certain fragment, or thenumber of words between two closest positions of a definite article, andmany others. Non-linearity of the signal changes depending on proximityto a reference sequence—i.e. significant variations in the signaloccurring at even slightest deviations from a reference sequence—isprovided by the use of the implausibility number, which is a logarithmof an HyPa multiplication number.

[0194] 10. HyGV-Method in Locating Particular Strings in BinarySequences

[0195] In certain cases, it may be useful to apply an HyPamultiplication number rather than implausibility/plausibility number, asthe former represents an additive value. This can be demonstrated by theexample of binary sequence identification by the HyGV-method, as shownbelow. Binary sequences are extensively used in information transmissionof signals in hardware-independent data formats, and the HyGV-methodoffers a new solution to the problem.

[0196]FIG. 35 shows a binary sequence of 180 bits (here, n—seesubsection 8 above—is the number of bits). Assume that the search targetis the set of 15 bits, from 35 through 49 (i.e. k=35, f=15), of thegiven sequence. An example of construction of a capsule of clones forbit string identification is illustrated in FIG. 36. (In reality, theremay be a variety of versions of a capsule of clones, with differentmultiplication numbers for the HyPa and producing non-contradictoryresults.) FIG. 30 gives an example of constructing a table to compute ahybrid similarity matrix and establish the “M” values corresponding toeach move of the screening frame along a binary sequence under analysis.FIG. 37 shows how the M value changes as the reference frame (k=35,f=15) is moving along the entire sequence until it locates the referencebinary string. For comparison purposes, the diagram also shows thesignal (M) in response to the surfing of the 10-bit screening frame(k=35, f=10). The dark dots correspond to f=15; and the open dots, tof=10.

[0197] If, instead of M values, we plot the implausibility numbers (lnM) against the ordinate axis, the result will be the same as shown inFIGS. 32 and 33—i.e. the M value drops to 0 at the moment when thereference sequence gets located. Thus, the HyGV-method can besuccessfully used for identification of any binary strings. As matrixhybridization (as per copending application titled “High-dimensionaldata clustering with the use of hybrid similarity matrices”, by LeonidAndreev) involves minimal relevant computational resources, theidentification of a binary sequence of any length (i.e. number offrames), within a given binary sequence, is determined by only adistance between a reference string and a beginning of a sequence ofbits.

[0198] In case of the use of M value (FIG. 37), 12 levels of signalvalues, differing from each other by a same value, are produced. The 12levels correspond to a metric known as and widely used in the technologyof information transmission as the Hamming distance (H) which equals thenumber of disagreeing bits between two binary vectors. Thus, when theframe 35-49 moves along the binary string shown in FIG. 35, the Hammingdistance equals 11 (FIG. 37).

[0199] 11. HyGV-Method and Mathematical Statistics

[0200] By the foregoing examples, illustrating various applications ofthe method of this invention, we have demonstrated that this inventionprovides a universal method for data processing that can be easilycustomized in accordance with any highly-specific data processing task.Multiplication numbers, employed in the method of this invention, playthe role of additive quantitative criteria allowing for a description ofobjects characterized by large sets of parameters. This makes thepresent invention especially valuable for biological sciences.

[0201] Understanding the processes responsible for living beingsfunctioning and, especially, cognition is pursued by many differentsciences, among which computer science plays a special and consolidatingrole due to its potentials in modeling of highly complex processes. Onthe one hand, computer science provides the necessary tools forresearchers in biology, but, on the other hand, it is obvious computerscience itself is under strong influence of biological knowledge andideas. Such a type of alliance between sciences is quite common: forexample, mathematics and biology, biology and engineering (bionics),biology and physics (biophysics). All of the said intersciencerelationships have one thing in common: on the one side, it is one ofthe sciences with traditionally extensive use of deductive approaches,and on the other side, biology, where induction dominates as a method ofcognition. Hence, the strong interest in biology-relevant theoreticalmodeling and the tendency in computer science to overusing toofundamentally-looking terms from the areas of biological levelcomplexity with respect to theoretically and technically simple models.

[0202] For many decades, mathematical statistics has been the venuewhere physico-mathematical sciences meet with the sciences of biologicallevel of complexity, including not only biology but also ecology,sociology, medicine, etc. This is not accidental, for mathematicalstatistics is the science that leans on the law that may be the onlyuniversal law adequately describing processes at the populationlevel—the law of probability distribution. The equations describing theprobability distribution at the population level (e.g. Gauss',Poisson's), overgrown with countless auxiliary techniques, have become apowerful instrument of mathematical analysis and, for many decades bynow, have been forced onto biology as the only criterion of validity ofquantitative measurements and scientific conclusions. Thus, a long pathof problem-solving—from coin flipping to most complex issues ofbiological sciences—has been cleared by mathematical statistics byartificially removing the major limitation of probabilistic approachesconsisting in the necessity to meet the requirement for equalprobability of events, which is nearly impossible for biology. It is notaccidental that the harsh criticism of the main paradigm of mathematicalstatistics, voiced in the above-cited work by Anderson et al. (Anderson,D. R., Burnham, K. P., and Thompson, W. L. (2000) Null hypothesistesting: problems, prevalence, and an alternative. Journal of WildlifeManagement 64(4): 912-923), comes from specialists in ecology, one ofthe most complex areas of biology, who wrote: “The fundamental problemwith the null hypothesis testing paradigm is not that it is wrong (it isnot), but that it is uninformative in most cases, and of relative littleuse in model or variable selection. Statistical tests of null hypothesesare logically poor (e.g., the arbitrary declaration of significance)”.Hallahan further opines that “rather than blindly assuming all data tofit NHST's (null hypothesis significance testing) underlyingassumptions, researchers should explicitly try to model the phenomenaunder investigation” (Hallahan, Mark. The hazard of mechanicalhypothesis testing. Psycoloquy, 1999, 10, #1 Social Bias 13. Italics inthe above quote are by L. Andreev and D. Andreev). The life scienceresearch community is getting increasingly disappointed withmathematical statistics, finding it primitive and helpless inapplication to complex biological problems. The limitations ofmathematical statistics in life sciences have become especially apparentdue to advances in computer programming: a plenitude of software toolsfor mathematical statistics, produced within the past decade, haveexhaustively covered all that has ever been conceived in this field andthus made it ultimately clear that mathematical statistics, despite itswealth of methods and techniques, has extremely limited potentials as aresearch tool for biological sciences.

[0203] This invention, formally a method for non-probabilisticstatistical processing, is capable of taking a role in cooperationbetween exact and biological sciences. Figuratively speaking, it is inthe tag line of the research work underlying this invention:non-biological intelligence (cf the materials published onhttp://www.matrixreasoning.com). The method of this invention offersthat missing link without which no smooth transition from biologicalsciences to exact sciences is possible. The two examples below provideillustrations to the above-stated.

[0204] FIGS. 38A-38F illustrate correlations, presented inbi-logarithmic coordinates, between multiplication numbers and theshares of male population of age groups of 00-04, 20-24, and 75-79, thereference objects being Saudi Arabia and Russia. Here, multiplicationnumbers serve as generalized criteria for each of the 220 objects(states) covering 34 parameters of population pyramids; thus eachindividual parameter can be correlated to such a generalized criterion.The data presented in FIGS. 38A-38F allow for the following conclusions.For instance, there is an obvious correlation between multiplicationnumbers and percentages of 0-4 and 75-79 age groups in population of theworld in general, whereas no such correlation exists for the 20-24 yearsage group; which may be explained by the fact that the former two groupsrepresent a so-called immobile part of population, while the latter isrepresented by a mobile group. Or, for example, another apparent fact isthat analyses of population pyramid data groups of countries in respectof their similarities with two different countries as referenceobjects—(a) Saudi Arabia and (b) Russia—produce opposite results; etc.

[0205] It goes without saying that a population pyramid of any countryis a complex cooperative system, and that none of its parameters shouldbe treated as an independent one, and that even though the total ofpercentages of different age groups is normalized to 100%, their actualtotal does not bear any scientific meaning—it would be senseless to sumup percentages of individual age groups as, in reality, the relationshipbetween them is governed by much more complex operations than simpleaddition and subtraction operations. In this respect, multiplicationnumbers used in the method of this invention are indispensable inprocessing of data of this type, as they have the property of additivityand are equal to the sums of values of individual parameters. Theyrepresent the vector that, as was mentioned above, furnishes informationthat allows for establishing correlations between various features ofcomplex biological systems; and, as is seen from the graphs shown inFIGS. 38A-38F, now that this part of analysis is done, classicalmathematical statistics can be meaningfully applied.

[0206] Another example to further explain about the correlations betweenmultiplication numbers and analyzed objects' properties is based onclimatic data analysis dealing with a set of objects, each of themdescribed by 108 parameters (see FIGS. 8 and 9). FIG. 39 shows, as anexample, the relationship between normal daily maximum temperatures(F.°) in February and multiplication numbers obtained for a database of245 U.S. locations described by 108 parameters and using Charleston,S.C., as a reference object. As is seen, there is a distinct correlationbetween the said parameter values for February and the multiplicationnumbers that reflect the totality of other 107 parameter values for thecities of a number of states (indicated by black dots in the graph).This correlation is valid for 33 central, east coast, and some of thesoutheast states: AL, AR, CT, D.C., GA, IA, IL, IN, KS, KY, MA, MD, ME,MI, MN, MO, MS, NC, ND, NE, NI, NJ, NY, OH, PA, RI, SC, SD, TN, VA, VT,WI, and WV. Thus, both of the above examples show that classicalmathematical statistics can have a perfect field of operation as soon asthe data have been processed by the method of this invention withoutwhich the establishing of correlations between any given individualparameters and a whole complex body of climatic parameters would beimpossible.

[0207] 12. The Interrelation Between HyGV and ETSM Methods

[0208] In all of the examples presented in this disclosure, the obtainedresults, produced by the HyGV system that does not undergo any training,display a remarkable agreement with the human logic. (Forself-explanatory reasons, in this disclosure, we have limited the numberof examples and illustrations that can prove the above-said.) The factof this remarkable agreement with the human logic is the more soimportant as the HyGV-method, based on the set of algorithms referred toin the section “Background of this invention”, acts as an independentexpert with its own strongly individual (however, manageable) style and,therefore, represents a promising system for development of the thinkingcomputer. The individuality of the HyGV-method is provided by thehypothesis-parameter module which constitutes its “ego”, or“personality”.

[0209] In the context of the above statement, it is important to explainhow the two methods—evolutionary transformation of similarity matrices,which is used as an engine in the HyGV-method, and the hypothesisgeneration and verification method itself, being the subject of thisinvention—cooperate in data processing. When the ETSM-method is appliedto a certain database, it produces a system of subclusters that reflectsa hierarchy in strictly a given set of objects as it analyzes individualrelationships in a given set of objects taking into account the wholeextent of diversity inherent in that particular community of objects andtreating a given dataset as a whole. It registers all nuances insimilarities and dissimilarities between properties of analyzed objects,and even a slight change within the structure of a system under analysismay result in major changes in clustering, either giving additionalsubclusters or, contrarily, merging some of the clusters. For example,assume that the ETSM-analysis reveals the existence of a certainsubcluster “a” whose elements have a high degree of affinity to eachother. In fact, it may appear that the elements in the said subclusterhave significant differences in values of parameter V_(i) but thosedifferences may stay unnoticed due to wide fluctuations in the values ofother parameters. If we add, to the analyzed system of objects, a fewobjects with, for instance, low V_(i), while leaving the distribution ofall other parameters unchanged, it may lead to the subdivision ofsubcluster “a” into two sub-subclusters—“a_(ihigh)” and“a_(ilow)”—according to their values of parameter V_(i). In the sameway, subclusters merge when appropriate.

[0210] In contrast to the ETSM-method that supplies objective butrelative (in terms of being related to a concrete dataset underprocessing) information, the HyGV-method provides subjective informationevaluated by its “ego”, an HyPa. Although that information issubjective, it has an absolute value—i.e. neither the addition of newobjects to a database under analysis, nor removal of any of its objectshas any impact on the M value; and even if all but one object belongingto a distinctly shaped cluster are removed, the remaining object will beallocated strictly to where it belongs. We will now explain it in moredetail. The HyGV-method analyzes each object in a given set of objectsfrom the point of view of its similarity with a reference object—or, ina broader sense, with a hypothesis-parameter. For instance, in theabove-discussed example, the countries of the world are analyzed fromthe position of their similarity-dissimilarity with Saudi Arabia, orwith Russia. At the same time, the results of HyGV-analysis representabsolute values—of course, in the context of a given underlying conceptor a given reference object. Thus, these two methods—ETSM andHyGV—employ different principles of data processing. The ETSM-method canbe used both as a clustering detector (information thyristor) and as asource of background information about the grouping in a set of objectsunder analysis, providing a clue for finding the most optimal concept tobe used as a hypothesis-parameter in the HyGV-analysis.

[0211] Hypothesis generation and verification are inseparable from theroutine cognitive activity of a human-being and take a very importantplace in human intelligence. The mimicking of human intelligence and theimplementation of the resulting models in the form of software cannot bedone based on just one of the aspects of brain activity. None of thenumerous models of cognitive processes proposed since the onset of theAI research was able to demonstrate the ability to combine reasoningwith intuition, which are both engaged in the human mind, just as twolungs are both engaged in respiration. Despite the centuries-longhistory of research (mostly at the speculative level) into relationshipbetween intuition and reasoning, the issue still remains very littleexplained, which is quite understandable as this is a problem of afundamental level of complexity. Since the ancient times, intuition hasbeen in the focus of philosophic studies and, particularly, ofdoctrinaires of all religious schools. Buddhism philosophy maintainsthat intuition, not reasoning, is a direct perception of Truth. Similarviews on intuition are shared by some of the modern philosophies, suchas intuitivism, neothomism, and others. It is also known that many aflash of scientific inspiration came from sheer “illogical” thinking,i.e. intuition. Thus, the issue of the relationship between reasoningand intuition cannot be ignored in tackling the modeling of the humanbrain work.

[0212] The combination of two data processing methods invented by us—theevolutionary transformation of similarity matrices, based on organiccombination of deduction and induction, and hypothesis generation andverification, involving the formation of the artificial “ego” and basedon the information thyristor principle—is a good model of thesimilarities and differences between intuition and reasoning, as isillustrated in FIG. 40. In data analysis, the ETSM-method is thecounterpart of intuition, and the HyGV-method does the reasoning part.The schematic diagram in FIG. 40 is the first concrete modelillustrating the comparative characteristics of intuition and reasoning.Reasoning is consistently subjective as long as the involved “ego”remains unchanged and it would be true to say the same about thereasoning performed by the HyGV-system according to this invention. Aperson's “ego” can change and evolve as a result of enforced learning, agoal-seeking activity, and in the course of one's gaining experience andknowledge throughout one's lifespan. In the HyGV-method, the “ego” is ahypothesis-parameter, and it also may change—either as a result of anoperator's command, or in the course of certain endogenous evolutionaryprocesses. For instance, if we duplicate one of the components of ahypothesis-parameter, the HyPa's clustering configuration will change,and, accordingly, multiplication numbers for the components will change.However, if a hypothesis-parameter remains unchanged for a given set ofobjects, multiplication numbers will remain exactly same, no matter howmany extra copies of objects are added to the database under analysis.

[0213] We will now consider how the ETSM-method reacts to changes in adatabase under analysis—for instance, the appearance of extra copies ofsome of the objects of the database. We will let the ETSM-system solvethe problem—quite a complex one—of clustering of a natural series ofnumbers from 1 through 24. A human mind, given the same problem, wouldmost probably think of either subdividing the set into equal parts, suchas 1-12 and 13-24, or of separating even and odd numbers. The decisionmade by the ETSM-system (XR-metric, B=1.50) is non-trivial andintelligent: it subdivides the dataset into two unequal subclusters: (1)numbers from 1 through 13; and (2) from 14 through 24 (see FIG. 41A).Subdivision into equal size groups (1-12 and 13-24) would produce twoequal clusters, and the clustering tree would not bear any sense. Now,if we add one additional “1” to the series of numbers (see FIG. 41B),the clustering picture drastically changes. While the first round ofdivision, just like in case of the original set of data, producesclusters by dividing the set of objects almost by half, it is, however,followed by three additional subdivisions, thus making the number ofsubclusters 6 instead of 2. FIGS. 41C-41H illustrate the changesoccurring to the clustering tree due to the addition of new units. Then,finally, all of the numerals (objects) again split into two clusters,one of which consists all the “1”'s, while the other contains all othernumbers (FIG. 41I).

[0214] When considering all 9 variants (selectively presented in FIG.41) of the clustering occurring in the process of adding “1”'s to theseries of natural numbers from 1 to 24, it is apparent that all of thevariants are logical in their own way. If the number of added “1”s isunknown, then it looks as if the set of produced decisions is equivalentto illogical thinking, as classical logic simply cannot accept such avariety of logical decisions. If the “ego” (i.e. a hypothesis-parameter)is disengaged, the whole totality of information available for a givenanalysis process is processed holistically—the way it is done by theETSM-method. Focusing on a certain portion of the available information(for instance, under the influence of the emotional factor) will lead toa series of analogies through redistribution of associations(relationships between clusters), thus jumpstarting the creativity. Thisis exactly what intuition is. As was mentioned above, intuition isbelieved to be primary as compared to reasoning. The same kind ofrelationship is between the ETSM-method, with its cooperative andholistic approach, and the HyGV-method, with its “ego” (in the form of ahypothesis-parameter) and its way of dealing with information byprocessing it through the channel of subjectivity.

[0215] The above-formulated model of the interrelation between intuitionand reasoning may serve not only the purpose of development of thethinking computer but also of the elaboration of practical approaches tomachine learning based on manipulating of the two constituents ofconsciousness—intuition and reasoning.

[0216] 13. Machine Self-Learning

[0217] Ability to learn is one of the most remarkable and mysteriousproperties of the intelligence. As was discussed above, the computationand application of implausibility number, ln M (i.e. the dissimilaritycriterion), provides for analogs ranking in accordance with theexponential change depending on the decrease of similarity between ananalog and a reference object. This opens a concrete and clear way forselective extraction of objects from databases. The method of thisinvention can provide not only passive sorting of information but it canalso be used for information retrieval based on certain preferences. Thelatter function may be implemented based on a hypothesis-parameter'sself-evolution, which represents a purely technical problem inaccordance with a self-learning stimulus, which may vary in every agiven task, as well as the technical implementations may do—one of thembeing, for instance, adding duplicates—one after another—of objects froma database under analysis to a relevant hypothesis, followed by theselection of such a hypothesis that provides the best solution to agiven problem. In HyGV-method, a hypothesis-parameter, which is thecentral platform of the non-biological reasoning system according tothis invention, easily undergoes modifications, either endogenously orexogenously—i.e. resulting from the operator's commands. This makes thehypothesis-parameter technique a very promising tool in theinvestigation of various aspects of theoretical and practical analysis.

[0218] The above-said is demonstrated by the examples illustrated inFIGS. 42A-42F. In this case, demographic data based on populationpyramids (34 parameters) were analyzed to reveal similarities anddissimilarities between 37 European countries with predominantlyChristian populations, Israel—with predominantly Judaic population(marked with dark dots), and 57 member-states of the Organization ofIslamic Conference (accordingly, countries with predominantly Muslimpopulations) (open dots). FIG. 42A shows the relationship betweenmultiplication numbers for the case when France is a reference object.Multiplication number values, in this case, correspond to HyPa values of3 and 1, respectively. As is seen, the countries with predominantlyChristian populations are quite distinctly differentiated from themember-states of the Organization of Islamic Conference. Now, we will beadding, one by one, the duplicates of objects (countries) to the capsuleof clones (CC) created for object “France”. FIG. 42B illustrates a newgrouping: after the addition of object “Uganda” to the CC. As is seen,the grouping of objects has considerably changed in this case. This factsupports the previously stated thesis about the possibility for the HyPato self-evolve, and the above-demonstrated example of its self-evolutionis the technically simplest way, consisting in a one-by-one addition ofduplicates of each object of a data set to the HyPa. Thus, in a quiteuncomplicated way, a database can be optimized and structured. However,results of such optimization must always be treated with a clearrealization of hierarchy of priorities in such self-evolution. Forinstance, with the database referred to in the above-discussed example,an objective of the analysis (i.e. an assignment) may consist in thedistinction between two major groups of countries based on theirpopulation pyramids. Priorities may be set differently: for instance,finding the simplest mathematical descriptions of the two groups ofcountries (see FIGS. 42A, B, and D), or demonstrating the biggestdifference between the two groups (cf FIGS. 42C and D), etc. Theobjective of the self-evolution and self-learning task may be theestablishing of invariants in the relationship between the objects(countries). HyPa automated self-modification allows the system to solvethis important problem most efficiently: it finds that two Muslimcountries of the Western hemisphere—Guyana and Suriname—always appearclose to each other, and that the same is true for some other groups ofcountries—e.g. Pakistan and Bangladesh, Malaysia and Indonesia, etc. Itis apparent that the above-described HyPa self-evolution techniqueprovides large opportunities in development of computer programs capableof reasoning operations.

[0219] The issue of self-learning stimuli for a hypothesis-parameter'sself-evolution is beyond the scope of this invention; it is a wholedifferent problem for which a lot of technical solutions may be found inaccordance with concrete tasks. One of the simplest solutions consistsin equipping a database with object samples provided with expertevaluation, in which case self-learning stimuli will be represented bycoefficients of agreement between results produced by the autonomously“reasoning” software and the expert opinion.

[0220] The ETSM-method, which serves as an engine for the HyGV-method,works independently and autonomously, by definition (see “Background ofthis invention”), and provides the stability and reproducibility of dataprocessing by the HyGV-method. The only factor of influence upon theinformation processing by the ETSM-method is multiplication of objects'parameters, according to copending application “High-dimensional dataclustering with the use of hybrid similarity matrices”, by LeonidAndreev (although, technically, it is input data which is influenced,rather than the way of processing). A hybrid similarity matrix mayinclude any proportions of monomer similarity matrices, thus providingthe means for regulating the multiplication numbers for ahypothesis-parameter, which also produces quite useful task-orientedeffects. Above (see FIG. 21), we demonstrated selective extraction of animage of a certain pose of a human figure from the database of 75images. The closest analogs of the reference object (query) were foundto be four images of poses: two of them with the right leg bent in theknee and raised, in the same fashion as the query object; and two otherposes with both legs positioned straight. FIG. 43 illustrates the resultof processing of the same query as above and using the samehypothesis-parameter, the only difference being the value of parameter“R-Toe” increased 10 times, which resulted in the following: (a) the twoimages of poses with the right leg and right hand raised as in the queryfigure remain at the exactly same point; (b) the two images with bothlegs straight are no longer among the query's analogs; instead, (c) thesearch engine finds two other images with the right leg raised as in thequery image. This example demonstrates how, using one and the samehypothesis-parameter, it is possible to increase the search selectivityby regulating the weights of various parameters. This preferredembodiment of the method of this invention allows forhypothesis-parameter's self-evolution, thus providing the most optimalapproach to data processing It is important to emphasize that in case ofa multi-dimensional description of objects, combinations of weightedparameters become increasingly numerous and resourceful.

[0221] Machine learning can occur in two ways: deductive (through setsof rules) and inductive (from sets of objects, e.g. through clusteringsimilar objects together) (Michalski, R. S., Carbonell, J. G., Mitchell,T. M., Machine Learning. Morgan Kaufmann Publishers, Los Altos, Calif.,1986). The combination of two methods, ETSM and HyGV, provides formachine self-learning in a fashion most closely mimicking humanself-learning, i.e. combining intuition, deduction and induction. Theabove-described machine self-learning techniques are technically simpleimplications of the method of this invention as all three approaches tomachine self-learning—intuitive, inductive and deductive—are provided bythe same algorithmic base and, therefore, are functionally compatible.

[0222] 14. Conclusions

[0223] Below we will formulate three important points that follow fromthe foregoing.

[0224] A. Computer as an “Independent Thinker”

[0225] As was emphasized in the Background of this invention, there isno chance that a computer may be a thinking one unless its softwareenvironment has its own individual “outlook” on the nature of objectsand events—in other words, has its “ego”. The method of this inventionprovides a computer program that is capable of selective perception ofinformation, thus making a leap to creation of the non-biologicalintelligence. The hypothesis-parameter, being the simplest form ofcomputer “ego”, can be easily developed into a more complex system ofindividual perception based on “ego-intranet” wherein individualhypotheses-parameters, as carriers of subjective perception, not onlycan co-exist but also can interact and cooperate.

[0226] B. Comparison Between the HyGV-Method and Artificial NeuralNetwork Method.

[0227] The numerous examples of the HyGV-method applications presentedin the foregoing sections of this disclosure demonstrate that the methodof this invention, based on rather a simple system of algorithms andimplemented on a regular PC, allows the solving of highly complex dataanalysis problems without the necessity to apply the feature extractionprocess for reduction of data dimensionality. The latter, beingcrucially important for the ANN-method due to its technical limitationsas far as memory and computation time are concerned, is also a source ofsubjectivism which makes automated data processing even morecomplicated. The ANN approach is based on assigning appropriate weightsto different inputs. The said procedure, even if fully automated, doesnot relieve the ANN-method from a too high computational load. TheHyGV-method, on the contrary, is reduced to the right choice of ahypothesis-parameter, after which weights of inputs are automaticallyestablished by the unsupervised clustering procedure. Thus, in theHyGV-method, computer training consists in mere memorization of an HyPaand parameters that describe objects or events under analysis. Even now,at this present stage of the HyGV-method development, it is clear thatthis method efficiently solves all and any of the problems heretoforetackled by the ANN-method. However, there is a significant differencebetween these two approaches. Even though the ANN-method refers to themultiple layers of simple processing as “neurons”, nonetheless, themethod remains to be an operator-dependent system, a section of computerprogramming and mathematics. Same problems, as the ANN-method is tosolve, can be successfully solved by mathematical methods that havenothing to do with AI (e.g. Fourier transformation, gradient descentapproaches, spline methods, etc.). The rapid development of the ANNconcept and the growth of its popularity have been mostly due to thebelief that an ANN system imitates the work of brain cells, which intruth is too big a stretch. The ANN-method is an utterly artificialsystem leading the non-biological intelligence research to a dead end,and it would be naive to hope that this artificial system canmiraculously become animated simply due to increased doses ofquasi-biological terminology, such as neurocontroller or alike (cf e.g.Werbos, U.S. Pat. No. 6,581,048, Jun. 17, 2003).

[0228] As was demonstrated in the examples presented in the foregoingsections of this disclosure, unlike the ANN systems, the HyGV systemdisplays immanent intelligence, and its decisions, in most cases,coincide with the human logic. This comes naturally due its organizationthat ensures the system's autonomous and cooperative work throughout thewhole analysis process that involves an entire set of parametersdescribing objects under analysis and is free from artificial censuringand reduction of data. Most importantly, the HyGV system displays theability of “individual perception” due to its “ego” embodied in theHyPa.

[0229] C. Information as a Processor of Information

[0230] There are two remarkable peculiarities displayed by the HyGVsystem in the process of multiplication number computation as describedin the foregoing specification. The first one is connected with the factthat in none of the cases of data analysis by the HyGV-method there wasobserved the presence of any sort of interval of the M values covering agradual transition of target objects into subclusters of referenceobjects. Instead of an expected S-shaped curve in the area of objectstransition, shown in blocks 503 and 504 of FIG. 5, the rigid transitionoccurs by the “yes-no” principle. Thus, in this invention, the methodfor evolutionary transformation of similarity matrices acts as a valvewith a very steep threshold of switching on/off, justifying thereby theterm “information thyristor”.

[0231] The second remarkable peculiarity of the method of this inventionlies in the fact that the said switching is controlled by informationitself Whether the infothyristor “GATE” is open or shut off, all of theoperations for construction of a hybrid similarity matrix and itsfurther transformation are absolutely same. However, it appears that acertain amount of batches of qualitatively identical information causesthe infothyristor turn on or off, thus creating new information thatreveals a conventional complexity degree or a degree of dissimilaritybetween a target object and a reference object. Although not discussedin the foregoing description of this invention, other forms ofinformational imitation of electronic circuit elements, for instance,the information transistor, are definitely possible (for the informationtransistor, the ETSM-method alone is sufficient). Thus, both theinfothyristor and infotransistor can function based on the enginedescribed in the specification of this invention.

[0232] The discovery of the informational analogs of electronic circuitelements raises a reasonable question: Could it be that theinformation's ability to process information and thus generate newinformation underlies many of the mysterious aspects of the brainfunctioning? It seems that this question cannot be avoided, since thecorrect answer—be it positive or negative—should have an extremelyfar-going impact on science in general. For instance, for long time, ithad been believed that intelligence was correlatable, although veryroughly, with the brain size. Nowadays, arguments for ‘brainsize—intelligence’ correlation based on, for instance, the fact that theskull of A. afarensis, an early predecessor of a human, was 3.5 timessmaller than the modern human skull are no longer sufficient for statingthat the larger is a brain, the higher is intelligence. Moreover,inferences from such a statement may be utterly absurd: are cats, whosebrain is half of the size of that of dogs, twice less intelligence thandogs? The above-expressed assumption that information can produceinformation-generating structures that resemble electronic chips shouldtransform the role of the brain into, hyperbolically speaking, thecounterpart of computer hardware (without a CPU). If this is so, thenthe brain size is, to a certain extent, a derivative of a set ofbehavioral stereotypes that is sufficient for a given species to survivein its natural habitat. The opportunity that we discovered in principlefor development of infochips may have important implications forcomputer science. Even purely theoretical discussions of thisopportunity may lead to serious transformations in traditional views onthe nature of information. The reasoning presented below may serve astheoretical substantiation of the possibility for existence ofinformation infoprocessors in the brain.

[0233] It is apparent that contemporary physics has trouble with theexplanation of what information is. A physicist's reply to the question“what is the physical essence of information?” typically consists inslogans of the type “There is no information without representation”(see e.g. http://www.aip.org/physnews/preview/1997/qinfo/sidebar4.htm),or lengthy general discussions augmented by mathematical sophistry inorder to smooth the shocking straightforwardness of the question. Eventhough the quantum computing idea has brought about some activation ofresearch into physical principles of information, no significantprogress in this area has been yet made. One of the few importantcontributions ever made into the problem of physics of information isthe idea proposed by Rolf Landauer in 1961 (Landauer, R. 1961. IBM J.Res. Develop. 5, 183) and known as “Landauer's principle”. As noted byM. B. Plenio and V. Vitelly (Plenio, M. B., and Vitelli, V. The physicsof forgetting: Landauer's erasure principle and information theory.Contemporary Physics, 2001, volume 42, number 1, pp. 25-60), Landauer'sprinciple “provides a bridge between information theory and physics”. Ofall the numerous aspects of the interpretation of Landauer's principle,in this context, we will focus on his statement that the erasure ofinformation, unlike computation (i.e. information production/recording),is inevitably accompanied by the generation of heat, i.e. involvesenergy expenditure. While it is an undeniable fact that physical systemsor media, such as CDs, electronic chips, DNA strands, and others arenecessary for information recording (in other words, indeed, “there isno information without representation”), it is also clear beyond anydoubt that, at least at the macro-level, the recording and erasure ofinformation cannot occur simultaneously on a same medium. This axiom,corroborated by Landauer's principle, is the quintessence of the maindistinguishing property of information as a physical phenomenon. When wespeak about logical and physical reversibility of computation, it isunderstood that recording and erasure of same information is separatedin time. It is impossible to both record and erase information at thesame time on a same carrier by a same device; and that is whatdistinguishes physics of information from physics of living andnon-living matter.

[0234] In living cells, there simultaneously occur twoprocesses—anabolism and catabolism, whose combination makes what isreferred to as metabolism, which constitutes the main distinguishingproperty of the alive. Anabolism provides for construction of complexmolecules, and catabolism results in their breakdown. Molecularconstruction and destruction go on at the same time, thus releasing theenergy for biochemical reactions. In living organisms, oxidation andreduction occur at the same time and are inseparable. When the anabolismrate equals the catabolism rate, an organism is in a steady state. Apopulation of bacteria may undergo a continuous growth or may simplymaintain a surviving state, and the ratio of anabolism versus catabolismmay greatly vary; however, anabolism cannot occur without catabolism. Inthe non-living nature, there is a similar phenomenon: synthesis mayprevail over degradation, or vice versa; but these processes always gotogether. Thus, information processes uniquely differ from thoseoccurring in living and non-living matters: it is characteristic ofinformation that its synthesis, or “anabolism” (i.e. recording) anddestruction, or “catabolism” (i.e. erasure) cannot occur at the sametime on a same medium.

[0235] The above-said obviously leads to the inference that in thebrain, neurons are used only for information storage (memory) with thepossibility for its retrieval; while information erasure can occur onlythrough neuron death (which happens first of all to neurons that forlong time remain unused for information retrieval). If informationprocessing in the brain is carried out by neural cells alone, thelatter, in order to function, have to renege on the general biologicalrule of the anabolism and catabolism co-occurrence. From thatstandpoint, it seems to be perfectly admissible to assume that theremust exist a special kind of information fields, similar in design toelectronic circuits and equipped with informational counterparts of theelements of electronic circuits. Such an infoprocessor system canreceive information, process it, store it, retrieve it from long-termmemory to create associative links, thus updating information, and sendit back for storage along with the newly created associative links. Ifthis is true, then an organism's intelligence power depends onperformance characteristics of its information processing system, butnot on a volume of information stored in neurons.

[0236] The foregoing also sheds light on the problem of the biologicalrole of sleep. The assumptions that sleeping is required for maintenanceof the metabolic system, to provide rest and repair of muscles and othertissues, replace aging or dead cells, lower energy consumption, etc. areunpersuasive for many reasons. All of those processes can and do occurin the awake state as well. Bacteria, for instance, can growcontinuously and do not need a sleep; though, in the course of apopulation growth, cells both grow and die. Although sleepiness isassociated with adenosine accumulation, high adenosine concentration ismerely a signal of a need to sleep. Likewise, many other differences inbiochemical processes related to the sleep and awake states have theirexplanations. In the meantime, from the standpoint of the existence ofinfoprocessors of information, the role of sleep should be understood asa natural state required for information erasure that cannot occurduring information recording. The erasure of useless informationaccumulated in the field of infoprocessors of information occurs duringREM sleep, and, since it involves energy expenditure, according toLandauer's principle, REM sleep duration is limited and, therefore, itoccurs in several short spans. During non-REM sleep, energy is producedfor the next erasure operation and for the necessary rearrangement ofthe information field structure.

[0237] In this context, we are not discussing the mechanism ofgenerating, by the brain cells, of structured information fields.However, a thorough research into this problem may lead to a revision ofthe role of synapses, provide the explanation of the unusual morphologyof neurons, and reveal a lot more. There is no doubt that the concept ofinfoprocessors of information as the main field of the processesproviding for the cognitive function of the brain has a high applicationpotential in medical research. For instance, in the view of the proposedtheory, Alzheimer's disease should be understood as the lowering or lossof the brain cells ability to generate information fields. A psychiatricdisorder manifested in schizophrenia symptoms may be due to breaches inthe process of information transmission from infoprocessors to long-termmemory, and so on. As for computer science, it can certainly benefitfrom the practical implementation of the infoprocessing concept due toits practically unlimited possibility to increase processing speed.

[0238] Although the description above contains many specifics, theseshould not be construed as limiting the scope of the invention but asmerely providing illustrations of some of the presently preferredembodiments of this invention. Thus the scope of this invention shouldbe determined by the appended claims and their legal equivalents.

What is claimed in this invention is:
 1. A computer-operable method fordata processing involved in but not limited to image, pattern andsequence recognition, decision-making and machine-learning, comprisingthe steps of: (a) generation of a hypothesis-parameter, in addition toparameters already existing in a database, both for a reference object(or reference objects) to be used as benchmark(s) in data processing,and for all other objects in a database that are subjects of acomparative analysis (hereunder referred to as target objects); (b)assigning of digital values to reference objects in ahypothesis-parameter, such digital values being a reflection of acertain hypothesis of a relationship between said referenceobjects—based on either an a priori existing idea or a result of apreliminary experimental study, including clustering, of objects coveredby said hypothesis-parameter; (c) assigning of certain digital values toall target objects in a hypothesis-parameter; (d) using ahypothesis-parameter in clustering of objects, along with plurality ofother parameters describing objects under clustering, (e) establishing anumber of copies (hereunder referred to as multiplication number) ofhypothesis-parameter required for compensation, during a clusteringprocess, of effect of all other parameters describing a given object sothat clustering based on thus established number of copies of ahypothesis-parameter along with the rest of parameters is identical toclustering produced upon use of a hypothesis-parameter as the onlyparameter, (f) consecutive addition of each target object to a referenceobject (or reference objects); and (g) using an establishedmultiplication number for measurement of dissimilarity between referenceobject(s) and target objects, hence verification of validity of ahypothesis underlying a generated hypothesis-parameter.
 2. The method ofclaim 1, wherein logarithm of hypothesis-parameter multiplication number(hereunder referred to as implausibility number) is used as adissimilarity coefficient in a search for closest analogs of a referenceobject.
 3. The method of claim 2, wherein, with the purpose ofincreasing the selectivity of a search for a reference object's closestanalogs, an internal standard is employed, and a degree or coefficientof similarity between a reference object and an object compared to it iscomputed as a ratio of: a difference between implausibility numbers ofan internal standard and a compared object, and an internal standard'simplausibility number.
 4. The method of claim 1, wherein, in order topredict multiplication numbers for target objects on which noinformation is available, multiplication numbers for such objects arecomputed as a total of increments corresponding to individual parametersdescribing objects of a given database, which allows configuring atarget object by combining different parts of different objects in adatabase.
 5. The method of claim 1, wherein a hypothesis-parameter of areference object (or reference objects) is developed for a variety ofobjects, artificially generated based on a reference object (orreference objects), whose totality represents a capsule of clones ofsaid reference object(s) and is further used for determining thedissimilarities between reference object(s) and target object(s).
 6. Themethod of claim 5, wherein a said capsule of clones is created invarious ways, including a monotonous increase or decrease, oralternation of increase and decrease, of values of all or part ofparameters that describe reference object(s).
 7. The method of claim 5,wherein, for the purpose of sequence or pattern recognition, ahypothesis-parameter is created by cloning a reference object whoseparameters represent input quantitative characteristics of elements of asequence or pattern under analysis.
 8. The method of claim 1, wherein amultiplication number of a hypothesis-parameter is established in anautomated unsupervised mode by applying the algorithm for evolutionarytransformation of similarity matrices, serving as an informationthyristor and providing fusion between a target object and a referenceobject (or reference objects) when a certain number of multiple copiesof a hypothesis-parameter is added to analyzed data.
 9. The method ofclaim 8, wherein, in order to ensure the accuracy of computation of ahypothesis-parameter multiplication number required for turning on aninformation thyristor and stopping a subjective and inefficient processof feature extraction for reduction of data dimensionality, similaritymatrices are computed by hybridization of monomer similarity matrices,which, in their turn, are computed individually based on each parameter,including a hypothesis-parameter.
 10. The method of claim 9, wherein, inorder to assess a weight of an individual parameter in a multiplicationnumber, extra copies of an individual parameter describing a targetobject are added, in the form of a monomer similarity matrix, to ahybrid similarity matrix.
 11. The method of claim 1, wherein, to providefor non-probabilistic statistical processing of data, a multiplicationnumber is used as a quantitative criterion of conventional complexity.12. The method of claim 1, wherein, to provide for machineself-learning, a hypothesis-parameter is designed to be able toself-evolve through various means, such as, for instance, a consecutiveaddition of target objects' duplicates, which, upon the use ofappropriate stimuli, provides for self-improvement of ahypothesis-parameter playing the role of the non-biological intelligence“ego”.
 13. The method of 8, wherein, in order to create artificial, ornon-biological, intelligence, information processing is performed byprocessors of the information thyristor type wherein information itselfserves as an information valve.
 14. The method of claim 13, wherein, toprovide an actual prototype of artificial brain, the method forevolutionary transformation of similarity matrices is used in itsintuition mode, when applied as is, and in the reasoning mode, whenapplied in combination with a hypothesis-parameter (artificialintelligence “ego”).